PMID- 30134473
OWN - NLM
STAT- MEDLINE
DCOM- 20190916
LR  - 20210615
IS  - 1869-0327 (Electronic)
IS  - 1869-0327 (Linking)
VI  - 9
IP  - 3
DP  - 2018 Jul
TI  - Feasibility of Electronic Health Record-Based Triggers in Detecting Dental 
      Adverse Events.
PG  - 646-653
LID - 10.1055/s-0038-1668088 [doi]
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.
CI  - Georg Thieme Verlag KG Stuttgart . New York.
FAU - Kalenderian, Elsbeth
AU  - Kalenderian E
AD  - Department of Preventive and Restorative Dental Sciences, School of Dentistry, 
      University of California, San Francisco, California, United States.
FAU - Obadan-Udoh, Enihomo
AU  - Obadan-Udoh E
AD  - Department of Preventive and Restorative Dental Sciences, School of Dentistry, 
      University of California, San Francisco, California, United States.
FAU - Yansane, Alfa
AU  - Yansane A
AD  - Department of Preventive and Restorative Dental Sciences, School of Dentistry, 
      University of California, San Francisco, California, United States.
FAU - Kent, Karla
AU  - Kent K
AD  - Department of Quality Improvement, School of Dentistry, Oregon Health and Science 
      University, Portland, Oregon, United States.
FAU - Hebballi, Nutan B
AU  - Hebballi NB
AD  - Department of Diagnostic and Biomedical Sciences, School of Dentistry, University 
      of Texas Health Science Center, Houston, Texas, United States.
FAU - Delattre, Veronique
AU  - Delattre V
AD  - Department of Diagnostic and Biomedical Sciences, School of Dentistry, University 
      of Texas Health Science Center, Houston, Texas, United States.
FAU - Kookal, Krisna Kumar
AU  - Kookal KK
AD  - Department of Diagnostic and Biomedical Sciences, School of Dentistry, University 
      of Texas Health Science Center, Houston, Texas, United States.
FAU - Tokede, Oluwabunmi
AU  - Tokede O
AD  - Department of Oral Health Policy and Epidemiology, Harvard School of Dental 
      Medicine, Boston, Massachusetts, United States.
FAU - White, Joel
AU  - White J
AD  - Department of Preventive and Restorative Dental Sciences, School of Dentistry, 
      University of California, San Francisco, California, United States.
FAU - Walji, Muhammad F
AU  - Walji MF
AD  - Department of Diagnostic and Biomedical Sciences, School of Dentistry, University 
      of Texas Health Science Center, Houston, Texas, United States.
LA  - eng
GR  - R01 DE022628/DE/NIDCR NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20180822
PL  - Germany
TA  - Appl Clin Inform
JT  - Applied clinical informatics
JID - 101537732
SB  - IM
MH  - Data Mining
MH  - *Dentistry
MH  - *Electronic Health Records
MH  - Feasibility Studies
MH  - Humans
MH  - Medical Errors
MH  - Patient Safety
PMC - PMC6105337
COIS- None.
EDAT- 2018/08/23 06:00
MHDA- 2019/09/17 06:00
PMCR- 2019/07/01
CRDT- 2018/08/23 06:00
PHST- 2018/08/23 06:00 [entrez]
PHST- 2018/08/23 06:00 [pubmed]
PHST- 2019/09/17 06:00 [medline]
PHST- 2019/07/01 00:00 [pmc-release]
AID - 180118ra [pii]
AID - 10.1055/s-0038-1668088 [doi]
PST - ppublish
SO  - Appl Clin Inform. 2018 Jul;9(3):646-653. doi: 10.1055/s-0038-1668088. Epub 2018 
      Aug 22.