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Patient Safety Tip
of the Week
February 18, 2020 More Nudges
We learned a lesson from commercial laboratories many years
ago. Those commercial labs made their money, of course, by doing more tests. So,
they packed their order forms for tests in a manner that made it most easy for
clinicians to check as many boxes as possible.
So, when we recognized at our hospital that many tests were
either unnecessarily repetitive or of low value, we changed our own lab and
radiology requisition forms (paper at the time) to remove from the front of the
form those tests that were of low value. We also looked at all our standardized
order sets and saw the same pattern. Those order sets that offered checkboxes
for “prn” medications almost always invited checking boxes.
And, when we did our first electronic medical record
implementation, we were extremely concerned that design of various screens and
menus could make it too easy to order things that might not be absolutely necessary. We discussed that in our March 23,
2010 Patient Safety Tip of the Week “ISMP Guidelines for Standard
Order Sets”. We noted that order sets often included prn orders for
sleep meds, laxatives, antacids, etc. Often, these were included so a clinician
did not have to be awakened at night to order such medications. ISMP’s
guideline on order sets also cautioned against including blanket-type orders (eg. laxatives, sleep meds, antiemetics, antidiarrheals,
antacids, etc).
Conversely, you can use form design to “nudge”
clinicians to remember to do certain things, like assessing DVT risk and
considering DVT prophylaxis.
We’ve done several columns on how use of “nudges” can
increase the likelihood of the most desirable action occurring. But sometimes
nudges can be in the wrong direction. That is, they push you to do something
that may actually be detrimental or at least not the
best course of action. We discussed these issues in our July 23, 2019 Patient Safety Tip of the Week “Order Sets Can Nudge the
Right Way or the Wrong Way”.
A recent study
looked at the impact of default settings for opioid prescribing in the
emergency department (Montoy
2020). The researchers
randomly altered the default settings for quantities of opioid tablets. These
changes were made without announcement, and prescribers were not informed of
the study itself. They found that an increase of 0.19 tablets prescribed was
found for each tablet increase in default quantity. They conclude that this
low-cost, easily implementable, EMR-based intervention could have far-reaching
implications for opioid prescribing and could be used as a tool to help combat
the opioid epidemic.
Chin and colleagues (Chin
2018) demonstrated similar findings for postprocedural prescribing of
opioids. The electronic medical record (EMR) can autopopulate
a default number of pills prescribed, and 1 potential method to alter
prescriber behavior is to change the default number presented via the EMR
system. In a multi-hospital system, they reduced the default number of opioid
pills autopopulated in the EMR when prescribing
discharge analgesia from 30 to 12. Clinicians could still alter the number of
pills prescribed to any amount they deemed necessary. After the default change,
the median number of opioid pills prescribed decreased from 30 to 20. The
percentage of prescriptions written for 30 pills decreased from 39.7% before
the default change to 12.9% after the default change, and the percentage of
prescriptions written for 12 pills increased from 2.1% before the default
change to 24.6% after the default change. This occurred without significant
change in opioid refill rates. This translated to a decrease of 5.22 opioid
pills per prescription and a total decrease of 34.41 morphine milligram
equivalents (MME’s) per prescription. The authors concluded this is a simple,
effective, cheap, and potentially scalable intervention to change prescriber
behavior and decrease the amount of opioid medication prescribed after
procedures.
Another recent study used an EMR “nudge” to reduce
inappropriate orders for C. diff testing (Howard-Anderson
2020). Orders for hospital-onset C. difficile infection testing in patients
administered a laxative or stool softener in <24 hours triggered an EMR
alert defaulting to cancellation of the order (“nudge”). Orders considered
inappropriate for C. diff testing decreased from 8% to 6% after implementation
of the “nudge”. Total monthly orders decreased by 21% postintervention.
In our April 30, 2019 Patient Safety Tip of the Week “Reducing Unnecessary Urine
Cultures” we noted how clinicians and researchers at Barnes-Jewish
Hospital implemented a program to reduce unnecessary cultures (Munigala 2019). Their
intervention consisted of notifications to providers, changes to order sets,
and inclusion of urine culture reflex tests in commonly used order sets. The
CPOE intervention they implemented was setting the default option to urine dipstick testing followed by a bacterial
culture if positive (i.e. reflex testing), rather than a culture alone, This resulted in a 45%
reduction in the urine cultures ordered. That intervention saved
approximately $104,000 in laboratory costs alone over the 15-month period plus
likely savings from reduced antibiotic use and less contribution to the
emergence of resistant organisms.
Note that the “nudge” need not necessarily go to the
physician. Another innovative intervention from the PennMedicine
Nudge Center imcreased screening rates for breast
cancer and colorectal cancer 22 percent and 14 percent, respectively (Hsiang
2019). The intervention was an active choice intervention in the electronic
health record that prompted medical assistants to inform patients about cancer
screening during check-in and template orders for clinicians to review during
the visit. Unfortunately, there were minimal changes in the rates of patients
who followed through within one year and completed their screenings. Perhaps a
“nudge” at the patient level is necessary as well. But the study demonstrated
that we can reduce the risk of alert fatigue for
physicians by sending the alerts to other members of the healthcare team. We’ve
previously noted that some alerts might be sent to pharmacists rather than
directly to physicians.
Most of the examples above are aimed
at overcoming one of the most powerful cognitive biases, the “default bias”. That bias demonstrates
our natural tendency to select default options when several options are
possible. Two related cognitive biases are the “inertia bias” and the “status
quo bias”, both of which encourage us to keep doing something. (Remember
that monthly fee for software you seldom use anymore that you keep forgetting
to cancel?). We discussed these in our July 7, 2009 Patient Safety Tip of the
Week “Nudge:
Small Changes, Big Impacts”.
The study by Chin et al. (Chin 2018) also raised the role of yet another cognitive bias, “anchoring”,
to the result produced by the new default. “Anchoring reflects the idea that
human adjustments of estimates are based on a given starting value. In this
case, estimates of the number of opioid pills needed for analgesia were
anchored around the default number of pills presented, and a new, lower anchor
helped decrease overall prescribing.”
Nudges can also be valuable in steering medication ordering
in a desirable way. A study by Patel et al. (Patel
2016) found that a change in the EMR default to generics instead of brand
names resulted in prescribing rates for generics increasing from 75% to 98%.
In another study, Patel and
colleagues (Patel 2018) found that nudges which asked clinicians to make an active
choice on statin prescriptions and delivered feedback on how each clinician’s
performance compared to their peers led to a significant increase in statin
prescription rates. Physicians received an email with a link to an online
information “dashboard” listing their patients who were eligible for statin
therapy but not already receiving it. One group of physicians also received a
note comparing their rate of prescribing statins for eligible patients to other
doctors in relevant specialties. Physicians in both intervention groups were
asked to review the list of patients and use a multi-choice menu to prescribe a
statin for each one or indicate a reason for non-prescribing. Results were
compared to a third group that received no “dashboard” list and no comparison
feedback.
The “dashboard” listed each of their patients along with
their age, sex, and the following data as available from the EHR: 10-year ASCVD
risk score, most recent LDL-C level, other lipid levels, body mass index,
history of smoking and any form of clinical ASCVD (eg,
myocardial infarction or stroke), liver function tests, and medical record
number. The PCPs were asked to review the list of patients within 1 week and
use the dashboard to select whether or not to
prescribe each patient a statin. The dashboard provided an overview of the
study, a link to the American College of Cardiology/American Heart Association
guidelines, and options for selecting statin dosage.
Compared with usual care, there was a significant increase
in statin prescribing in the active choice with peer comparison arm (adjusted
difference in percentage points, 5.8), but not in the active choice arm.
The authors felt that a key element of their intervention
design was active choice framing. Physicians could review patients using the
automated patient dashboard and make decisions on statin prescriptions. By
offering multiple options to prescribe a guideline-indicated statin and
requiring a reason to say no, clinicians may be nudged toward prescribing a
statin. Peer comparison feedback delivered just once by email was effective at
increasing physician engagement with the automated patient dashboard. One
advantage of this approach is that it does not require waiting for a patient to
come to the clinic to make a decision. It also might
optimize future clinic visits by removing the identification of eligible
patients and process of prescribing so that the physician and patient can focus
more on other aspects of care. A potential downside is that physicians with larger
patient panels may face difficulties managing these types of decisions outside
of their traditional clinic model when they receive a long list of eligible
patients.
Mitesh S. Patel, M.D., head of Penn Medicine’s “Nudge Unit”,
and colleagues wrote an informative review on nudges in the New England Journal
of Medicine (Patel
2018b). They describe nudges as ways of presenting choices in ways that
strongly influence consumer behavior. They give some great practical examples
from everyday life. Airlines require consumers to actively choose whether to
purchase trip insurance before they can buy a plane ticket. Amazon displays
additional, complementary items alongside the purchase you are about to make. Video
streaming sites often use default settings to automatically play the next
episode in a TV series to encourage binge watching.
They go on to describe how nudges can
be designed to remind, guide, or motivate behavior in healthcare. They suggest
targeting areas in which suboptimal care can be addressed by targeting a
specific decision that drives a less-than-optimal behavior. For example, when
prescribing medications, physicians must decide between brand-name and generic
formulations. They cite the study we noted above (Patel 2016) that found a change in the EMR default to generics instead of
brand names resulted in prescribing rates for generics increasing from 75% to
98%.
They note suboptimal
referral of eligible patients for cardiac rehabilitation after myocardial
infarction may have resulted from an opt-in system. So, they redesigned he
process as an opt-out system in which referral for rehab was the default. The
referral rate increased to more than 80%.
Thoughtful design of order entry screens and standardized
order sets is important in helping physicians make correct choices and avoid
less optimal ones. An editorial by Vaughn and Linder (Vaughn
2018) discussed 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:
- When a new order set is
created, is influence on clinician behavior considered?
- Which options are listed
for testing and treatment? All options? Or just clinically appropriate
ones?
- How are they listed?
Alphabetically? Numerically? Or are recommended and less-expensive options
listed first?
They stress the strong effect of using appropriate default
settings, citing the study mentioned above (Munigala
2018) that successfully reduced inappropriate urine cultures in an
emergency room by changing the default option from “urinalysis with reflex to
urine culture” to “urinalysis with reflex to microscopy”.
Of course, there are other interventions that can be used to
reduce the amount of unnecessary tests. Education and
audit and feedback may modestly reduce the ordering of “routine” tests. A
recent study (Ambasta
2020) found an 11% reduction in “routine” lab tests ordered in an
academic setting when residents were given education and feedback with
comparison to peers and attending internists were given feedback with
comparison to peers.
But education and training always
rank low in our hierarchy of effective interventions. In our January 2020 What's
New in the Patient Safety World column “ISMP Canada: Change Management to Prevent
Recurrences” we noted the
excellent editorial by Soong and Shojania “Education
as a low-value improvement intervention: often necessary but rarely sufficient”
(Soong 2019). It describes several scenarios and how education fits in with
each. In our brief illustrative Power Point “RCA Strong vs. Weak Responses” we show education and training as the
weakest actions. We don’t include “nudges” in that hierarchy because that list actually applies to RCA actions. But nudges would probably
fit somewhere near the middle of a list of action effectiveness.
The strongest actions are forcing functions and constraints.
How about this for an effective constraint: Don’t Pay for It! In a study on
low-value tests, Henderson et al. (Henderson
2020) looked at the impact of Choosing Wisely recommendations for vitamin D
testing in Canada and the US. These resulted in 4.5% and 14.5% reductions in
testing in Ontario and the US, respectively. But Ontario implemented a payment
policy change that eliminated reimbursement of vitamin D screening. That was
associated with a 92.7% relative reduction in such screening!
To be effective, any “nudge” needs to be inexpensive to
implement, nonobtrusive to the clinician, and provide useful information that helps
lead to active choices. You need to be very wary you don’t simply create
“noise” that leads to alert fatigue. Equally important is to establish a method
by which you will evaluate whether the nudge actually
produced desired results and, just as importantly, did not produce any
unintended consequences.
See
some of our other columns dealing with “nudges”:
- July 7, 2009 “Nudge: Small Changes, Big Impact”
- February 18, 2018 “Nudged, But Who Nudged Who?”
- June 19, 2018 “More EHR-Related Problems”
- April 30, 2019 “Reducing Unnecessary Urine Cultures”
- July 23, 2019 “Order Sets Can Nudge the Right Way or the Wrong Way”
References:
Montoy JCC, Coralic
Z, Herring AA, Clattenburg EJ, Raven MC. Association
of Default Electronic Medical Record Settings With
Health Care Professional Patterns of Opioid Prescribing in Emergency
Departments: A Randomized Quality Improvement Study. JAMA Intern Med 2020;
Published online January 21, 2020
https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2759133
Chin AS, Jean RA, Hoag JR, et al. Association of Lowering
Default Pill Counts in Electronic Medical Record Systems With
Postoperative Opioid Prescribing. JAMA Surg 2018; Published online July 18,
2018
Howard-Anderson JR, Sexton ME, Robichaux
C, et al. The impact of an electronic medical record nudge on reducing testing for
hospital-onset Clostridioides difficile infection.
Infection Control and Hospital Epidemiology 2020; Published online February 10,
2020
Munigala S, Rojek
R, Wood H, et al. Effect of changing urine testing orderables
and clinician order sets on inpatient urine culture testing: Analysis from a
large academic medical center. Infection Control and Hospital Epidemiology 2019;
Published online: 21 February 2019: 1-6
Hsiang EY, Mehta SJ, Small DS, et al. Association of an
Active Choice Intervention in the Electronic Health Record Directed to Medical
Assistants With Clinician Ordering and Patient
Completion of Breast and Colorectal Cancer Screening Tests. JAMA Netw Open 2019; 2(11) :e1915619
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2755489?resultClick=1
Patel MS, Day SC, Halpern SD, et al. Generic Medication
Prescription Rates After Health System–Wide Redesign of Default Options Within
the Electronic Health Record. JAMA Intern Med 2016; 176(6): 847-848
https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2520677
Patel MS, Kurtzman GW, Kannan S, et al. Effect of an
Automated Patient Dashboard Using Active Choice and Peer Comparison Performance
Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized
Clinical Trial. JAMA Netw Open 2018; 1(3): e180818
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2688535?resultClick=3
Patel MS, Volpp KG, Asch DA. Nudge
Units to Improve the Delivery of Health Care
N Engl J Med 2018; 378: 214-216
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6143141/
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
Ambasta A, Ma IWY, Woo S, et al.
Impact of an education and multilevel social comparison–based intervention
bundle on use of routine blood tests in hospitalised
patients at an academic tertiary care hospital: a controlled pre-intervention
post-intervention study. BMJ Quality & Safety 2020; Published Online First:
10 February 2020
https://qualitysafety.bmj.com/content/early/2020/02/10/bmjqs-2019-010118
Soong C, Shojania KG. Education as
a low-value improvement intervention: often necessary but rarely sufficient. BMJ Quality & Safety 2019; Published Online
First: 16 December 2019
https://qualitysafety.bmj.com/content/early/2019/12/16/bmjqs-2019-010411
Our Power Point: RCA - Strong vs. Weak Responses
http://patientsafetysolutions.com/docs/RCA_strong_vs_weak_responses.ppt
Henderson J, Bouck Z, Holleman R,
et al. Comparison of Payment Changes and Choosing Wisely Recommendations for
Use of Low-Value Laboratory Tests in the United States and Canada. JAMA Intern
Med. Published online February 10, 2020
https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2760344
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