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.

Diagnostic errors are increasingly recognized as a source of preventable harm in medicine, yet little is known about their occurrence in dentistry. The aim of this study was to gain a deeper understanding of clinical dental faculty members' perceptions of diagnostic errors, types of errors that may occur, and possible contributing factors. The authors conducted semi-structured interviews with ten domain experts at one U.S. dental school in May-August 2016 about their perceptions of diagnostic errors and their causes. The interviews were analyzed using an inductive process to identify themes and key findings. The results showed that the participants varied in their definitions of diagnostic errors. While all identified missed diagnosis and wrong diagnosis, only four participants perceived that a delay in diagnosis was a diagnostic error. Some participants perceived that an error occurs only when the choice of treatment leads to harm. Contributing factors associated with diagnostic errors included the knowledge and skills of the dentist, not taking adequate time, lack of communication among colleagues, and cognitive biases such as premature closure based on previous experience. Strategies suggested by the participants to prevent these errors were taking adequate time when investigating a case, forming study groups, increasing communication, and putting more emphasis on differential diagnosis. These interviews revealed differing perceptions of dental diagnostic errors among clinical dental faculty members. To address the variations, the authors recommend adopting shared language developed by the medical profession to increase understanding.

Background Patient-reported outcome measures provide an essential perspective on the quality of health care provided. However, how data are collected, how providers value and make sense of the data, and, ultimately, use the data to create meaningful impact all influence the success of using patient-reported outcomes. Objectives The primary objective is to assess post-operative pain experiences by dental procedure type through 21 days post-procedure as reported by patients following dental procedures and assess patients’ satisfaction with pain management following dental surgical procedures. Secondary objectives are to: 1) assess post-operative pain management strategies 1 week following dental surgical procedures, as recommended by practitioners and reported by patients, and 2) evaluate practitioner and patient acceptance of the FollowApp.Care post visit patient monitoring technology (FollowApp.Care). We will evaluate FollowApp.Care usage, perceived usefulness, ease of use, and impact on clinical workload. Design and methods We describe the protocol for an observational study involving the use of the FollowApp.Care platform, an innovative mobile application that collects dental patients’ assessments of their post-operative symptoms (e.g., pain). The study will be conducted in collaboration with the National Dental Practice-based Research Network, a collective Network of dental practices that include private and group practices, public health clinics, community health centers and Federal Qualified Health Centers, academic institutional settings, and special patient populations. We will recruit a minimum of 150 and up to 215 dental providers and up to 3147 patients who will receive push notifications through text messages FollowApp.Care on their mobile phones at designated time intervals following dental procedures. This innovative approach of implementing an existing and tested mobile health system technology into the real-world dental office setting will actively track pain and other complications following dental procedures. Through patients’ use of their mobile phones, we expect to promptly and precisely identify specific pain levels and other issues after surgical dental procedures. The study’s primary outcome will be the patients’ reported pain experiences. Secondary outcomes include pain management strategies and medications implemented by the patient and provider and perceptions of usefulness and ease of use by patients and providers.

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.