For being long-time advocates of healthcare IT as an
important patient safety tool, it sure seems we write a lot about the
unintended consequences of that same IT! See the numerous columns listed below
for some of the issues we’ve discussed. Some more unintended consequences
recently made the medical literature.
One involved a problem arising from use of an order set for
acute MI (Gupta
2018). That order set included beginning a beta-blocker, which happened to
be contraindicated in the actual patient for whom it was ordered. Its use led
to worsening acute heart failure and cardiogenic shock in this patient.
Note that this order set had been developed 5 years earlier
and had been reviewed and updated annually. House staff were encouraged to use
this “opt in” order set for patients with STEMI. The order set included use of
a beta-blocker because such use was originally a performance measure on
admission and discharge (the hospital did not change the order set when it had
later abandoned that performance measure). Also, though the admission note
indicated the physician would withhold beta blockers because of heart failure
and complete heart block, the "carvedilol" option on the order set
was selected because it was the first and most visible option in the
beta-blocker ordering window. The admitting physician also noted he was
influenced by the clinical decision support (CDS) message stating that
administering beta-blockers was a performance measure.
There were likely also communication errors between the
interventional cardiology team and the inpatient cardiology team, resulting in
the orders being given before the inpatient team knew all relevant information
about the patient.
In the accompanying editorial (Shah
2018) Shah and Cifu note we are often slow to
abandon outdated medical practices that are not based upon robust evidence.
Our May 3, 2016 Patient
Safety Tip of the Week “Clinical
Decision Support Malfunction”
highlighted a study from Brigham and Women’s Hospital (BWH) in Boston about
some disturbing findings on malfunctions of CDSS alerts (Wright
2016). Alarmingly, they found that the alert malfunctions were often very
difficult to detect and some had eluded detection for
long periods of time (weeks or even years!). Moreover, the causes for the
malfunctions were sometimes even more difficult to elucidate. They were,
however, able to identify several contributing factors:
These cases serve as a reminder that
you must have a very active multidisciplinary team the oversees your clinical
decision support systems (CDSS), even if you contract with an outside vendor
for CDSS services. Each time you introduce a new clinical decision rule you
need to review its use and impact within several weeks. You are looking
primarily to see how often that rule is overridden or ignored and the reasons
for that so you can determine whether or not to keep
the rule. But once you’ve decided to keep the rule, you need to review it periodically.
That should be at least annually and at any time there is a change in the
evidence base. The vendors that most hospitals currently contract with for CDSS
may issue updates every 6-12 months, but they often do not respond immediately
when there is a change in the evidence base that might impact a specific rule.
That is why you need to have your own active multidisciplinary team to act when
there is a change in the evidence underlying the rule(s). The same applies to
order sets. When there is a significant change or new finding in the evidence
base, you need to review your standardized order sets that might be impacted by
those changes. That means you need to have in place a detailed inventory of items from every order
set. For example, let’s say the FDA comes out with a warning about a particular drug. You need to be able to readily identify any
of your existing order sets that contain reference to that drug. So you need to have an easily “searchable” library of your order sets.
The Gupta case also highlights the problem that order sets
often harbor items that are otherwise hard to detect. A good example of buried
items comes from our discussions on inappropriate abbreviations (see our
Patient Safety Tips of the Week for July 14, 2009 “Is
Your 'Do Not Use' Abbreviations List Adequate?” and December 22,
2015 “The
Alberta Abbreviation Safety Toolkit”). In that column we noted we had found
pre-existing standardized order sets were a hidden source of many dangerous
abbreviations.
In yet another study from Brigham and Women’s Hospital and Partners Healthcare, Wong and
colleagues analyzed data on medication-related CDSS overrides in the ICU (Wong
2018). They found the overall appropriateness rate for overrides was 81.6%
and varied by alert type. More potential and definite ADE’s (adverse drug
events) were identified following inappropriate overrides compared with
appropriate overrides (16.5 vs 2.74 per 100 over-ridden alerts). However,
inappropriate overrides were over six times as likely to be associated with
potential and definite ADE’s, compared with appropriate overrides (OR 6.14).
They suggest that further efforts should be targeted efforts such as suppressing
alerts that are appropriately over-ridden.
We have echoed the advice of many others that, in order to avoid alert fatigue, you limit the number of
alerts your CDSS system triggers for clinicians and focus on those that are
most important from a patient safety perspective.
We’ve also often emphasized that it’s important to rout
alerts to the person most likely to intervene. That may not always be the
physician. Often sending an alert to a pharmacist, who may then intervene with
the ordering physician and suggest alternatives, may be the most appropriate
routing of some clinical decision rules.
Howe and colleagues (Howe 2018) analyzed
patient safety reports from the Pennsylvania Patient Safety Authority database
from 2013 through 2016. They found 557 reports (0.03% of all reports) which had
language suggesting the EHR potentially contributed to patient harm: potentially
required monitoring to prevent harm (84%, n = 468), potentially caused
temporary harm (14%, n = 80), potentially caused permanent harm (1%, n = 7),
and could have required intervention to save a life or could have resulted in
death (<1%, n = 2). They defined 7 categories of “usability factors”, with
the following distribution of reports:
Regarding clinical
processes, errors occurred during order placement (38%), medication administration
(37%), review of results (16%), and documentation (9%).
One potential
limitation of the study is that it only included reports which identified one
of the top five EHR vendors/products.
Thoughtful design of order entry screens and standardized
order sets is important in helping physicians make correct choices and avoid
less optimal ones. A recent editorial by Vaughn and Linder (Vaughn
2018) discusses how “nudges” may
be helpful. They note some designs provide a stimulus to do the wrong thing.
For example, simply providing a checkbox may nudge a physician to check that
checkbox. Providing the brandname of a drug may nudge
the physician to order the more expensive formulation rather than a generic
equivalent. And allowing a test to be ordered repetitively (eg.
“daily CBC”) may lead to inappropriate testing.
They suggest the following questions be asked during design
of order sets or order entry screens:
They stress the strong effect of using appropriate default
settings. They cite a study that successfully reduced inappropriate urine
cultures in an emergency room (Munigala
2018) by changing the default option from “urinalysis with reflex to
urine culture” to “urinalysis with reflex to microscopy”. (See also our Patient
Safety Tips of the Week for July 7, 2009 “Nudge:
Small Changes, Big Impact” and February 18, 2018 “Nudged,
But Who Nudged Who?” for examples of use of “nudges” in healthcare.)
And the issue of wrong patient events related to the EHR
just won’t go away. We’ve discussed this in numerous columns. A 54-year old man
died following routine knee surgery due to medications prescribed in the EHR
that were intended for a different patient (Minion 2018). An
anesthetist in Australia, while attending a new patient in the OR, opened the
record of a previous patient to prescribe fluids necessary to “keep the line
open” for intravenous antibiotics that had earlier been forgotten. But he
forgot to close that patient’s electronic medical record. The anesthetist then
prescribed doses of fentanyl meant for the patient currently in the OR but
entered these by mistake in the open record of that previous patient.
Apparently the HER did present multiple alerts while he was prescribing but all
were overridden by the anesthetist, selecting ‘consultant’s decision’ and
entering his password each time.
Interestingly, several of the key
features of EHR’s that we have previously described (see our May 20, 2008 “CPOE Unintended Consequences – Are Wrong
Patient Errors More Common?”)
do not seem to have played a role in the error(s) in this case. The patient’s
name was apparently displayed on all screens in this case. There was nothing to
suggest juxtaposition errors or truncation errors. And it does not appear that
two patient records were open at the same time or that 2 different applications
were open at the same time. But the last feature we highlighted, failure to log off, was obviously the
major problem here. We suspect that interventions such as those developed by
Adelman and colleagues, including the ID-verify alert (prompt with name, age,
gender and MD must verify) and ID-reentry function (MD must re-enter patient’s
initials, age, gender) might have prevented the patient misidentification in
the current case (see our August 1,
2017 Patient Safety Tip of the Week “Progress on Wrong Patient Orders”).
Lastly, it’s important to remember that EHR upgrades or
conversions from one EHR to another represent times of vulnerability to errors.
The Brigham and Women’s Hospital has had one of the most robust and most
studied EHR’s from the standpoint of patient safety. Several of our columns (including
references above in today’s column) have pointed out how it’s clinical decision
support tools and alerts have been fine-tuned to reduce low-impact alerts and
help avoid alert fatigue. But recently the system converted from their existing
legacy EHR to a commercial EHR and some unintended consequences were found (Wright
2018). Though the knowledge base and content of drug-drug
interaction (DDI) alerts was substantially the same between the two systems,
the researchers found a striking drop off in the acceptance rates for DDI
alerts after the conversion. Overall
interruptive DDI alert burden increased by a factor of 6 from the legacy EHR to
the commercial EHR. The acceptance rate for the most severe alerts fell from
100 to 8.4%, and from 29.3 to 7.5% for medium severity alerts. After disabling
the least severe alerts, total DDI alert burden fell by 50.5%, and acceptance
of Tier 1 alerts rose from 9.1 to 12.7%. The researchers felt that the decrease
in acceptance rates could not be fully explained by differences in the clinical
knowledge base or by alert fatigue associated with increased alert burden.
Instead, they felt that workflow factors played an important role. These included
timing of alerts in the prescribing process, lack of differentiation of more
and less severe alerts, and features of how users interact with alerts.
Information technology remains one of our most important
patient safety tools. But there are important lessons learned in all these
cases that can help us all avoid unintended consequences of our IT
interventions.
See some of our other
Patient Safety Tip of the Week columns dealing with unintended consequences of
technology and other healthcare IT issues:
References:
Gupta A, Das SR, Pandey A. β-Blockers in
Myocardial InfarctionIssues With Standard Admission
Order Sets. JAMA 2018; 319(12): 1269-1270
Shah SD, Cifu AS. From Guideline
to Order Set to Patient Harm. JAMA 2018; 319(12): 1207-1208
https://jamanetwork.com/journals/jama/article-abstract/2676090?redirect=true
Wright A, Hickman T-T T, McEvoy D, et al. Analysis of
clinical decision support system malfunctions: a case series and survey. JAMIA
2016; First published online: 28 March 2016
http://jamia.oxfordjournals.org/content/early/2016/03/28/jamia.ocw005
Wong A, Amato MG, Seger DL, et al. Prospective evaluation of
medication-related clinical decision support over-rides in the intensive care
unit. BMJ Qual Saf Published Online First: 09
February 2018. doi: 10.1136/bmjqs-2017-007531
http://qualitysafety.bmj.com/content/early/2018/02/09/bmjqs-2017-007531
Howe JL, Adams KT, Hettinger AZ, Ratwani
RM. Electronic Health Record Usability Issues and Potential Contribution to
Patient Harm. JAMA 2018; 319(12): 1276-1278
https://jamanetwork.com/journals/jama/article-abstract/2676098?redirect=true
Vaughn VM, Linder JA Thoughtless design of the electronic
health record drives overuse, but purposeful design can nudge improved patient
care. BMJ Qual Saf 2018; Published Online First: 24
March 2018
http://qualitysafety.bmj.com/content/early/2018/03/24/bmjqs-2017-007578
Munigala S, Jackups
RR, Poirier RF, et al Impact of order set design on urine culturing practices
at an academic medical centre emergency department. BMJ
Qual Saf 2018; Published Online First: 20 January
2018
http://qualitysafety.bmj.com/content/early/2018/01/19/bmjqs-2017-006899
Minion L. Electronic prescribing error in month-old EHR
responsible for death of NSW man, State Coroner finds. Healthcare IT News 2018;
06 April 2018
Wright, A, Aaron, S,
Seger, D. et al. Reduced Effectiveness of Interruptive Drug-Drug Interaction
Alerts after Conversion to a Commercial Electronic Health Record. J Gen Intern
Med 2018; first online 15 May 2018
https://link.springer.com/article/10.1007%2Fs11606-018-4415-9#citeas
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