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

Elsbeth Kalenderian, Enihomo Obadan-Udoh, Alfa Yansane, Karla Kent, Nutan B. Hebballi, Veronique Delattre, Krisna Kumar Kookal, Oluwabunmi Tokede, Joel White, Muhammad F. Walji

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)646-653
Number of pages8
JournalApplied Clinical Informatics
Volume9
Issue number3
DOIs
StatePublished - Jul 1 2018

Fingerprint

Electronic Health Records
Tooth
Health
Tissue
Allergies
Data mining
Infection
Patient Harm
Pain
Data Mining
Patient Safety
Patient Selection
Consensus
Hypersensitivity
Wounds and Injuries

Keywords

  • adverse events
  • dentistry
  • harm
  • patient safety
  • triggers

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

Cite this

Kalenderian, E., Obadan-Udoh, E., Yansane, A., Kent, K., Hebballi, N. B., Delattre, V., ... Walji, M. F. (2018). Feasibility of Electronic Health Record-Based Triggers in Detecting Dental Adverse Events. Applied Clinical Informatics, 9(3), 646-653. https://doi.org/10.1055/s-0038-1668088

Feasibility of Electronic Health Record-Based Triggers in Detecting Dental Adverse Events. / Kalenderian, Elsbeth; Obadan-Udoh, Enihomo; Yansane, Alfa; Kent, Karla; Hebballi, Nutan B.; Delattre, Veronique; Kookal, Krisna Kumar; Tokede, Oluwabunmi; White, Joel; Walji, Muhammad F.

In: Applied Clinical Informatics, Vol. 9, No. 3, 01.07.2018, p. 646-653.

Research output: Contribution to journalArticle

Kalenderian, E, Obadan-Udoh, E, Yansane, A, Kent, K, Hebballi, NB, Delattre, V, Kookal, KK, Tokede, O, White, J & Walji, MF 2018, 'Feasibility of Electronic Health Record-Based Triggers in Detecting Dental Adverse Events', Applied Clinical Informatics, vol. 9, no. 3, pp. 646-653. https://doi.org/10.1055/s-0038-1668088
Kalenderian, Elsbeth ; Obadan-Udoh, Enihomo ; Yansane, Alfa ; Kent, Karla ; Hebballi, Nutan B. ; Delattre, Veronique ; Kookal, Krisna Kumar ; Tokede, Oluwabunmi ; White, Joel ; Walji, Muhammad F. / Feasibility of Electronic Health Record-Based Triggers in Detecting Dental Adverse Events. In: Applied Clinical Informatics. 2018 ; Vol. 9, No. 3. pp. 646-653.
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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.",
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N2 - 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.

AB - 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.

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