Patient Safety Tip
of the Week
March 13, 2018 Intercepting Errors
Errors are inevitable. Therefore, a good patient safety
system recognizes that errors occur and seeks to identify those errors before
they reach the patient. We, of course, already have some interventions that
intercept errors and protect patients. Some examples are (1) bedside medication
verification systems using barcoding, (2) independent double checks, (3) having
a pharmacist and a nurse check the order prescribed by a physician before a
drug gets dispensed and administered, (4) clinical decision support systems
that create alerts during CPOE (eg. when a dose is
higher than the expected range for “usual” doses).
But sometimes even those tools that are designed to
intercept errors may fail and a medication may be administered to a patient in
error. There is still time, however, in some circumstances to mitigate the
effects of that error and spare the patient harm. That is especially likely
when the medication is given slowly over a period of time,
such as by IV infusion.
So researchers at Cincinnati Children’s Hospital Medical
Center developed a system for real-time identification of medication
administration errors and were able to demonstrate the efficacy of that system
in preventing patient harm (Ni
2018). They focused on reconciling 10 high-risk continuous intravenous
infusions and medications prescribed to NICU inpatients: total parenteral
nutrition (TPN), lipids, intravenous fluids (IVF), insulin, morphine, fentanyl,
milrinone, vasopressin, dopamine, and epinephrine.
Previous studies have shown that continuous intravenous
infusion has a higher risk and severity of error than other medication
administrations. The pediatric population, and especially a NICU population, is
ideal for such a system since so many drugs are given based upon patient weight
and dosage calculations and therefore are prone to error. In addition, intravenous
infusions usually span multiple nursing shifts and involve complex dosage
adjustments that are not captured by in-place interventions such as BCMA.
The researchers developed a “data extractor” module that extracted data about medications from
4 separate sources (entered medication orders, structured order modifications
that adjusted the original doses/rates via CPOE, medication administration
records that documented actual doses/rates administered to patients, and free-text
orders communicated from physicians to nurses that delivered complex dose/rate
adjustments). Then they developed a “detector”
module that identified discrepancies in doses/rates between MAR’s and other
data sources. If a discrepancy was identified, the module would trigger a medication
administration error (MAE) event with a summary and suggestion. (A 30-minute
grace period was allotted for verbal orders to allow for delays in processing
these orders.) A “notifier” module then
sent a message about the suspected MAE event to the clinician via a secure messaging
platform and the clinician would decide whether the event was truly an MAE
event.
Among the targeted medications/infusions, epinephrine had
the highest MAE rate, followed by TPN, IVF, morphine, and lipid. Five
medications had no associated MAEs. The frequency of dose adjustments varied
between medications/infusions during patient care. In
particular, most adjustments for TPN, lipid, and IVF were delivered via
free-text communication from physician to nurse. There was also a moderate
positive correlation between error rate and number of dose adjustments.
The automated MAE detection system achieved an overall sensitivity
of 85.3% and positive predictive value (PPV) of 78.0%. Sensitivity was >75%
across all medications/infusions except lipid and morphine, where one lipid and
one morphine MAE each was missed. With adjustments, the system achieved 100%
PPV for the majority of the medications/infusions and
>75% for those with frequent dose adjustments (epinephrine, TPN, and IVF).
However, there was a very high negative correlation between PPV and number of
free-text dose adjustments.
The automated system detected 84% of MAE events that
represented overdose or underdose, and 100% of MAE
events that represented significant overdose.
Chart review of MAE events detected by the automated MAE
detection system then helped identify system problems underlying the errors. The
78.0% PPV achieved by the system suggests that for every 10 error
notifications, 2 were false positive alarms. This relatively low false positive
rate is encouraging in averting alert fatigue.
Overall, the automated system detected 86.7% of clinical
errors that reached patients and, importantly, captured all rare but
substantial dosing errors. Though the most substantial reductions were realized
for long-time intravenous medications/infusions such as TPN and lipid, the
system has the potential to reduce harm exposure significantly for all
medications via real-time messaging technology.
This is an exciting development that has the potential to
help us intercept errors that have occurred before significant harm comes to
the patient, thus adding yet an additional layer of defense against patient
harm.
Note also that, unlike the dangers we’ve discussed regarding
texting orders, this is an example of the positive potential of text messaging
in health care. It is similar to the alerts sent to
responsible parties that we’ve discussed in our numerous columns on alarm
management systems.
We look forward to refinements of this system and
extrapolation to other patient populations and healthcare settings. Nice work!
References:
Ni Y, Lingren T, Hall ES, et al.
Designing and evaluating an automated system for real-time medication
administration error detection in a neonatal intensive care unit. Journal of the American Medical Informatics
Association 2018; Published: 10 January 2018
https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocx156/4797402
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