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

Abstract

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.

Keywords

triggers - adverse events - dentistry - harm - patient safety

Note

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Institutional Review Board of each participating institution.

• References

• 1 Kohn L, Corrigan J, Donaldson M. , eds; Institute of Medicine Committee on Quality of Health Care in America. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy of Science; 2002
• 2 Richardson WC. , ed; Institute of Medicine Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001
• 3 Leape LL, Berwick DM, Bates DW. What practices will most improve safety? Evidence-based medicine meets patient safety. JAMA 2002; 288 (04) 501-507
• 4 Ramoni RB, Walji MF, White J. , et al. From good to better: toward a patient safety initiative in dentistry. J Am Dent Assoc 2012; 143 (09) 956-960
• 5 Ramoni R, Walji MF, Tavares A. , et al. Open wide: looking into the safety culture of dental school clinics. J Dent Educ 2014; 78 (05) 745-756
• 6 Obadan EM, Ramoni RB, Kalenderian E. Lessons learned from dental patient safety case reports. J Am Dent Assoc 2015; 146 (05) 318-326
• 7 Bhardwaj A, Ramoni R, Kalenderian E. , et al. Measuring up: implementing a dental quality measure in the electronic health record context. J Am Dent Assoc 2016; 147 (01) 35-40
• 8 Resar RK, Rozich JD, Classen D. Methodology and rationale for the measurement of harm with trigger tools. Qual Saf Health Care 2003; 12 (Suppl. 02) ii39-ii45
• 9 Naessens JM, O'Byrne TJ, Johnson MG, Vansuch MB, McGlone CM, Huddleston JM. Measuring hospital adverse events: assessing inter-rater reliability and trigger performance of the Global Trigger Tool. Int J Qual Health Care 2010; 22 (04) 266-274
• 10 Classen DC. , et al. Development and evaluation of the Institute for Healthcare Improvement Global Trigger Tool. J Patient Saf 2008; 4 (05) 169-177
• 11 Hibbert PD, Molloy CJ, Hooper TD. , et al. The application of the Global Trigger Tool: a systematic review. Int J Qual Health Care 2016; 28 (06) 640-649
• 12 Unbeck M, Lindemalm S, Nydert P. , et al. Validation of triggers and development of a pediatric trigger tool to identify adverse events. BMC Health Serv Res 2014; 14: 655
• 13 Resar R. IHI Outpatient Adverse Event Trigger Tool. November 4, 2016. Volume 4. Available at: http://www.ihi.org/resources/Pages/Tools/OutpatientAdverseEventTriggerTool.aspx . Accessed January 28, 2018
• 14 Kalenderian E, Walji MF, Tavares A, Ramoni RB. An adverse event trigger tool in dentistry: a new methodology for measuring harm in the dental office. J Am Dent Assoc 2013; 144 (07) 808-814
• 15 Maramaldi P, Walji MF, White J. , et al. How dental team members describe adverse events. J Am Dent Assoc 2016; 147 (10) 803-811
• 16 Perea-Pérez B, Santiago-Sáez A, García-Marín F, Labajo-González E, Villa-Vigil A. Patient safety in dentistry: dental care risk management plan. Med Oral Patol Oral Cir Bucal 2011; 16 (06) e805-e809
• 17 Thusu S, Panesar S, Bedi R. Patient safety in dentistry - state of play as revealed by a national database of errors. Br Dent J 2012; 213 (03) E3-E3
• 18 Classen DC, Resar R, Griffin F. , et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011; 30 (04) 581-589
• 19 Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010; 363 (22) 2124-2134
• 20 American Dental Association. Code on Dental Procedures and Nomenclature (CDT Code). April 17, 2016 2015 . Available at: http://www.ada.org/en/publications/cdt/ . Accessed January 28, 2018
• 21 Kalenderian E, Ramoni RL, White JM. , et al. The development of a dental diagnostic terminology. J Dent Educ 2011; 75 (01) 68-76
• 22 Kalenderian E, Ramoni RB, White JM. , et al. The importance of using diagnostic codes. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2011; 112 (01) 4-5
• 23 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381
• 24 Cunningham M. More than just the kappa coefficient: a program to fully characterize inter-rater reliability between two raters. Paper presented at the SAS Global Forum 2009, Gaylord National Resort and Convention Center, Washington, DC
• 25 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33 (01) 159-174
• 26 Chorney JM, McGrath P, Finley GA. Pain as the neglected adverse event. CMAJ 2010; 182 (07) 732