Table 30 'Electronic Trigger Tool - Surveillance of Adverse Drug Events'

PSI 29: Electronic Trigger Tool - Surveillance of Adverse Drug Events
Origin: PSI by SimPatIE
Dimension Description
Description of Specific
Aspects of Patient Safety
Adverse drug events (ADEs) are continually placing patients at
risk of harm. ADEs are the single most frequent adverse event
type. Tracking the occurrences of ADEs over time is a useful way
to tell about the development of safety related to medication.
Thus use of specified "triggers" or clues – signalising that an ADE
might have occurred – is a suitable patient safety measure.
Aim of the PSI This PSI is intended to flag rates of ADEs.
Level of Determination of
Patient Safety
Safety is assessed at the aggregated patient level.
Source(s) Manual chart review has been considered the "gold-standard" for
identifying adverse events in many patient safety studies. The
methodology is expensive and has shown imperfect (46).
Automated surveillance for adverse drug events has been
demonstrated firstly by Classen et al. in the early 1990s (47).
Since then more groups have developed electronic methods
suitable for detecting adverse events based on the use of
"triggers", coded data, free-text clinical narratives, or a
combination of techniques (47-53). Advances in such electronic
systems will facilitate our ability to monitor adverse events (46).
Thus this PSI is based upon a computerised screening tool that
searches free-text discharge summaries for trigger words
representing possible adverse drug events.
Extent of Clinically Testing To assess the accuracy and define the epidemiology of computer
based medication error reports a retrospective cohort study of 581
error reports containing 1010 medication errors was conducted.
Of medication errors reviewed, 298 (30%) were prescribing
errors, 245 (24%) were dispensing errors, 410 (41%) were
administration errors, and 57 (6%) involved medication
administration records (MAR). Following expert review the
overall distribution of error type categories did not change
significantly, although only MAR errors were underreported by
the reporters. The researchers concluded “despite clear
imperfections in the data captured, medication error reporting
tools are effective as a means of collecting reliable information on
errors rapidly and in real time. Our data suggest that
administration errors are at least as common as prescribing errors
in children” (49).
A recent study by Murff et al. of the development of an electronic
trigger tool was based on a cohort study including 424 randomly
selected admissions. All discharge summaries with a trigger word
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present underwent chart review by two independent physician
reviewers. The presence of adverse events was assessed using
structured implicit judgment. A random sample of discharge
summaries without trigger words was reviewed too. It was found
that 59% of the discharge summaries contained trigger words.
Based on discharge summary review, 44.8% (327 of 730) of the
alerted trigger words indicated a possible adverse event. After
medical record review, the tool detected 131 adverse events. The
sensitivity and specificity of the screening tool were 69% and
48%, respectively. The positive predictive value of the tool was
52%. The study showed that the computerised screening method
offers researchers and quality managers a means to routinely
detect adverse events (50).
The use of Trigger Tools appears to increase the rate of ADE
detection approximately 50-fold over traditional reporting
methodologies. This result is based upon a retrospective review of
patient records (52). This result is supported by another study
using the Trigger Tool in a neonate ICU. The researchers found
that the rate of adverse event was substantially higher than
previously described. Many adverse events resulted in permanent
harm and the majority of events were classified as preventable.
Only 8% of the ADEs were identified using traditional voluntary
reporting methods (54).
It has been found, that the use of the trigger tool decreased patient
harm significantly (55)
Evidence of Clinically use of
Standards
The following standard has been used by IHI: “Decrease the
number of ADEs per 1000 doses by 75 percent within 1 year”
Indicator category Theme Related PSI: “Medication Errors”.
Data definitions The total number of ADEs per 1000 doses.
Numerator Description The total number of ADEs identified in a (defined) sample of
patient records.
Denominator Description Total number of medication doses administered to the patient
records reviewed.
Data Source Applying Trigger Tool for measuring the frequency of adverse
drugs events to patient’s records.
Identifying the institutional
context
The assessment of and development of safety related to
medication is important in general clinical and organisational
improvement policies.
Care Setting The indicator applies for medication safety.
Professionals Responsible for
heath care
Doctors and cares.
Lowest Level of Health Care
Delivery Addressed
Individual clinical units or departments.
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Allowance for Patient
Factors
Not applicable.
Stratification by Vulnerable
Populations
Not applicable.
Standard of Comparison Comparison over time can be made. No set time frame for
comparison has been identified.
Scoring Scoring is made according to the Electronic Trigger Tool chosen
e.g. (50)