Finding Dental Harm to Patients through Electronic Health Record-Based Triggers.

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.

Comparison of two at-home whitening products of similar peroxide concentration and different delivery methods.

PURPOSE: This study compared the whitening efficacy, side effects, and patients' preferences/perceptions of two whitening systems of similar peroxide concentration but different formulation and delivery methods. METHODS: The tooth color change of 24 participants was measured using a shade guide (BSG) and a spectrophotometer (ES). Color difference was calculated: ΔE* = [(ΔL*)(2) + (Δa*)(2) + (Δb*)(2)](1/2). One whitening treatment was randomly applied to the right or left maxillary anterior teeth and the other was applied to the contralateral teeth, at-home with 35% carbamide peroxide in a tray (TW) or with 14% hydrogen peroxide in strips (WS). The tooth color was evaluated at baseline, 15 and 30 days (15 days postwhitening). Participants rated their tooth and soft tissue sensitivity (1-10 scale) and completed a questionnaire on their preferences. Results were analyzed by repeated measurement regression analysis/Tukey and Mann-Whitney (p<0.05). RESULTS: At 15 days, the teeth treated with TW and WS presented ΔE* = 7 and 6, respectively (ΔBSG=3 for both), and at 30 days, they presented ΔE* = 7.5 and 6.5, respectively (ΔBSG=3 for both). There was no significant difference in tooth and soft tissue sensitivity between treatments. No participant reported tooth and gingival sensitivity at the postwhitening appointment. Of the participants, 83% preferred the TW over WS. CONCLUSION: Both ΔE* and ΔBSG showed no significant difference in tooth color change between TW and SW at either time point. By the end of the study no participants reported tooth and gingival sensitivity. Participants preferred TW over SW.