Building Consensus for a Shared Definition of Adverse Events: A Case Study in the Profession of Dentistry.

BACKGROUND: To achieve high-quality health care, adverse events (AEs) must be proactively recognized and mitigated. However, there is often ambiguity in applying guidelines and definitions. We describe the iterative calibration process needed to achieve a shared definition of AEs in dentistry. Our alignment process includes both independent and consensus building approaches. OBJECTIVE: We explore the process of defining dental AEs and the steps necessary to achieve alignment across different care providers. METHODS: Teams from 4 dental institutions across the United States iteratively reviewed patient records after identification of charts using an automated trigger tool. Calibration across teams was supported through negotiated definition of AEs and standardization of evidence provided in review. Interrater reliability was assessed using descriptive and κ statistics. RESULTS: After 5 iterative cycles of calibration, the teams (n = 8 raters) identified 118 cases. The average percent agreement for AE determination was 82.2%. Furthermore, the average, pairwise prevalence and bias-adjusted κ (PABAK) was 57.5% (κ = 0.575) for determining AE presence. The average percent agreement for categorization of the AE type was 78.5%, whereas the PABAK was 48.8%. Lastly, the average percent agreement for categorization of AE severity was 82.2% and the corresponding PABAK was 71.7%. CONCLUSIONS: Successful calibration across reviewers is possible after consensus building procedures. Higher levels of agreement were found when categorizing severity (of identified events) rather than the events themselves. Our results demonstrate the need for collaborative procedures as well as training for the identification and severity rating of AEs.

Classifying Adverse Events in the Dental Office.

BACKGROUND: Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office. METHODS: Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications. RESULTS: A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient. CONCLUSIONS: Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office.

Feasibility of Electronic Health Record-Based Triggers in Detecting Dental Adverse Events.

BACKGROUND: We can now quantify and characterize the harm patients suffer in the dental chair by mining data from electronic health records (EHRs). Most dental institutions currently deploy a random audit of charts using locally developed definitions to identify such patient safety incidents. Instead, selection of patient charts using triggers and assessment through calibrated reviewers may more efficiently identify dental adverse events (AEs). OBJECTIVE: Our goal was to develop and test EHR-based triggers at four academic institutions and find dental AEs, defined as moderate or severe physical harm due to dental treatment. METHODS: We used an iterative and consensus-based process to develop 11 EHR-based triggers to identify dental AEs. Two dental experts at each institution independently reviewed a sample of triggered charts using a common AE definition and classification system. An expert panel provided a second level of review to confirm AEs identified by sites reviewers. We calculated the performance of each trigger and identified strategies for improvement. RESULTS: A total of 100 AEs were identified by 10 of the 11 triggers. In 57% of the cases, pain was the most common AE identified, followed by infection and hard tissue damage. Positive predictive value (PPV) for the triggers ranged from 0 to 0.29. The best performing triggers were those developed to identify infections (PPV = 0.29), allergies (PPV = 0.23), failed implants (PPV = 0.21), and nerve injuries (PPV = 0.19). Most AEs (90%) were categorized as temporary moderate-to-severe harm (E2) and the remainder as permanent moderate-to-severe harm (G2). CONCLUSION: EHR-based triggers are a promising approach to unearth AEs among dental patients compared with a manual audit of random charts. Data in dental EHRs appear to be sufficiently structured to allow the use of triggers. Pain was the most common AE type followed by infection and hard tissue damage.

How dental team members describe adverse events.

BACKGROUND: Although some patients experience adverse events (AEs) resulting in harm caused by treatments in dentistry, few published reports have detailed how dental providers describe these events. Understanding how dental treatment professionals view AEs is essential to building a safer environment in dental practice. METHODS: The authors interviewed dental professionals and domain experts through focus groups and in-depth interviews and asked them to identify the types of AEs that may occur in dental settings. RESULTS: The initial interview and focus group findings yielded 1,514 items that included both causes and AEs. In total, 632 causes were coded into 1 of the 8 categories of the Eindhoven classification, and 882 AEs were coded into 12 categories of a newly developed dental AE classification. Interrater reliability was moderate among coders. The list was reanalyzed, and duplicate items were removed leaving a total of 747 unique AEs and 540 causes. The most frequently identified AE types were "aspiration and ingestion" at 14% (n = 142), "wrong-site, wrong-procedure, wrong-patient errors" at 13%, "hard-tissue damage" at 13%, and "soft-tissue damage" at 12%. CONCLUSIONS: Dental providers identified a large and diverse list of AEs. These events ranged from "death due to cardiac arrest" to "jaw fatigue from lengthy procedures." PRACTICAL IMPLICATIONS: Identifying threats to patient safety is a key element of improving dental patient safety. An inventory of dental AEs underpins efforts to track, prevent, and mitigate these events.

The dangers of dental devices as reported in the Food and Drug Administration Manufacturer and User Facility Device Experience Database.

BACKGROUND: The authors conducted a study to determine the frequency and type of adverse events (AEs) associated with dental devices reported to the Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. METHODS: The authors downloaded and reviewed the dental device-related AEs reported to MAUDE from January 1, 1996, through December 31,2011. RESULTS: MAUDE received a total of 1,978,056 reports between January 1, 1996, and December 31, 2011. Among these reports, 28,046 (1.4%) AE reports were associated with dental devices. Within the dental AE reports that had event type information, 17,261 reported injuries, 7,777 reported device malfunctions, and 66 reported deaths. Among the 66 entries classified as death reports, 52 reported a death in the description; the remaining were either misclassified or lacked sufficient information in the report to determine whether a death had occurred. Of the dental device-associated AEs, 53.5% pertained to endosseous implants. CONCLUSIONS: A plethora of devices are used in dental care. To achieve Element 1 of Agency for Healthcare Research and Quality's Patient Safety Initiative, clinicians and researchers must be able to monitor the safety of dental devices. Although MAUDE was identified by the authors as essentially the sole source of this valuable information on adverse events, their investigations led them to conclude that MAUDE had substantial limitations that prevent it from being the broad-based patient safety sentinel the profession requires. PRACTICAL IMPLICATIONS: As potential contributors to MAUDE, dental care teams play a key role in improving the profession's access to information about the safety of dental devices.

Antemortem dental records: attitudes and practices of forensic dentists.

This study was designed to provide insight concerning the attitudes and practices of forensic dentists regarding antemortem dental records reviewed for purposes of dental identification. Forensic dentists were invited to participate in a 10 item survey. The majority of the respondents reported a considerable amount of experience in dental identifications of the deceased. Sixty-six percent reported having suspected dental negligence or fraud in their antemortem record reviews. Only 17% believe that a forensic dentist should report suspected dental negligence, while 31% agree that dental fraud should be reported. Their response to additional issues addressed in the study suggests diversity in the practices and attitudes of forensic dentists in the use of antemortem dental records. In conclusion, opening a dialogue among practicing forensic dentists may lead to a standardized set of recommendations by the appropriate societies in the forensic dental community.