Pediatric Informatics: Computer Applications in Child Health (Health ...

30 Error Reporting Systems 393
30.5.3 Automatic Triggers
A new method for identifying errors is via “automatic triggers.” Triggers are
computerized data that may be associated with an error or adverse event (such as an
order for naloxone as a signal that an overdose of morphine has occurred). Unlike
most other error identification programs discussed, this type of error detection can
occur in real-time (such as monitors within CPOE systems). Thus, triggers have the
potential to prevent worsening clinical situations while a patient is still hospitalized.
A computerized surveillance system monitors for the occurrence of any trigger, as
defined by the hospital or outpatient clinic. 33,34 Typically the triggers are from lab
values (e.g. hypoglycemia, hyperkalemia) medication records (e.g. reversal agents
like naloxone), microbiology laboratory triggers (e.g. new positive blood culture),
radiology triggers (e.g. use of the word “fall” in the study’s justification) or administrative
data (e.g. readmission within 24 h). 11
With electronic clinical information, these types of triggers can be an excellent
way to augment a robust adverse event detection program. Triggers can augment
chart reviews (both paper and electronic), allowing more effective and timeefficient
pickup of errors than random or unscreened reviews. 35 Other advantages of
triggers include a consistent methodology for identifying adverse events over time.
This consistency can produce more reliable results after interventions are made to
address the adverse events. Therefore the progress of an institution or clinic can be
tracked and trended.
The use of natural language processing (NLP) to detect adverse events is evolving.
The goal of NLP is to convert electronic narrative documents into a coded
form suitable for computer based analysis. The coding is based on pattern matching.
Therefore documents such as discharge summaries, clinical notes and daily
progress notes can be searched for the occurrence of adverse events. 36
Triggers have been used to develop a successful (nonelectronic) error identification
program in neonatology that identifies errors at higher rates than existing
methods. Researchers found a positive predictive value of each identified trigger
to be 38%. Since neonatal intensive care units are complex environments, the possibility
for error is quite high (Chapter 4). In a published study, the trigger-based
program captured several previously unidentified instances of nosocomial infection,
catheter infiltrates, abnormal cranial imaging and unplanned extubations.
Although this program used manual chart review, the methodology can be adapted
to an automated electronic form. 37 This approach has been extended to other areas
of inpatient pediatrics. 43
Outpatient use of triggers is also possible. As the use of electronic medical
records increases in outpatient pediatric practices the ability to utilize triggers
grows. Outpatient triggers have been utilized in an outpatient geriatric setting 38 and
in other primary care clinics. 39
Barriers to computer based screening to identify errors and adverse events are
the low positive predictive value of their results. Therefore it is important to verify
the accuracy of the system. Both internal and external validations are important. 11