ArticleJDR clinical and translational research2020
Finding Dental Harm to Patients through Electronic Health Record-Based Triggers.
Article in JDR clinical and translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Implementation and Evaluation of the OWLL Intervention to Improve the Quality and Safety of Pediatric Dental Sedation: A Mixed-Methods Approach.Journal of patient safety · 2026Article
- Silent threat: A case on dental drill head aspiration.Radiology case reports · 2025Article
- Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel ClinRad System: Results from A Retrospective Observational Cohort.medRxiv : the preprint server for health sciences · 2025Article
- Article
- A scoping review of patient safety checklists in dentistry.Clinical oral investigations · 2024Article
- Dental patients as partners in promoting quality and safety: a qualitative exploratory study.BMC oral health · 2024Article
- Evaluating and improving the usability of a mHealth platform to assess postoperative dental pain.JAMIA open · 2024Article
- Foreign body aspiration and ingestion in dental clinic: a seven-year retrospective study.Journal of dental anesthesia and pain medicine · 2022Article
- Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records.Applied clinical informatics · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundPatients 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).
methodsWe 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.
resultsOf 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.
conclusionBy 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.
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