Nudges for Opioid Reduction After Major Surgery
Phase: NA
Conditions: Surgery
Sponsor: University of Washington

 
1 
 
 
 
 
 
 
 
 
 
 
Nudges for Opioid Reduction after Major Surgery (NORMS) Trial 
 
 
 
 
 
 
 
 
 
Version and date: V1.0 June 1, 2022 
 
 
 
 
 
ClinicalTrials.gov: NCT04927351 
 
 
 
 
 
Funding: This work was supported by a training grant from the National Institute of 
Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under 
award number T32DK070555. This funding source had no role in the design of this 
study and will not have any role during its execution, analyses, interpretation of the 
data, or decision to submit results. 
 
 

 
2 
Table of Contents 
 
1.0 Key Personnel 
3 
 
2.0 Background and Rationale 
5 
 
3.0 Study Aims 
5 
 
4.0 Design and Methods 
6 
 
 
4.1 Intervention 
7 
 
 
4.2 Outcomes 
9 
 
 
4.3 Study Schedule 
10 
 
5.0 Data Management and Information Security  
11
 
 
6.0 Statistical Analysis Plan  
12 
 
 
6.1 Randomization Overview 
12 
 
 
6.2 General Analytic Strategy 
12 
 
 
6.3 Sample Size and Statistical Power 
13 
 
7.0 Data and Safety Monitoring Plan (DSMP) and Study-Related SAEs and AEs 
15 
 
8.0 Protection of Human Participants 
17 
 
9.0 References  
18
 
 
 
 
 
 
 
 
 
 
 

 
3 
1.0 Key Personnel 
 
Lead Investigator:  
Irene Y. Zhang, MD 
 
 
 
 
University of Washington  
Department of Surgery 
1959 NE Pacific Street, Box 356410  
Seattle, WA 98195 
Phone: 206-616-5325 
Email: izhang@uw.edu 
 
Steering Committee: 
David R. Flum, MD MPH 
University of Washington  
Department of Surgery 
1959 NE Pacific Street, Box 356410  
Seattle, WA 98195 
Phone: 206-616-5440  
Email: daveflum@uw.edu 
 
Joshua M. Liao, MD MSc 
University of Washington 
Department of Medicine 
1959 NE Pacific St 
Seattle, WA 98195  
Phone: 206-616-6934 
Email: joshliao@uw.edu 
 
Saurabh Khandelwal, MD 
University of Washington  
Department of Surgery 
1959 NE Pacific Street  
Seattle, WA 98195 
Phone: 206-685-7126 
Email: skhandel@uw.edu 
 
 
 
 
 
Paul R. Sutton, MD PhD 
University of Washington 
Department of Medicine 
1959 NE Pacific St 
Seattle, WA 98195  
Phone: 206-616-1692 
Email: plsutton@uw.edu 
 
KC Gary Chan, PhD 
University of Washington School of Public Health 
Department of Biostatistics 

 
4 
Room 344, Box 351617 
Hans Rosling Center for Population Health 
3980 15th Avenue NE  
Seattle, WA 98195-1617 
Phone: (206) 685-9177 
Email: kcgchan@uw.edu 
 
 
 
 
 

 
5 
2.0 Background and Rationale 
 
Prescription medications to manage pain, nausea, and related symptoms are routinely 
prescribed after surgery. These represent an important part of postoperative care for 
patients after they leave the hospital. Opioid medications, in particular, are commonly 
prescribed in this setting. However, postoperative prescribing patterns for opioids in the 
U.S. are highly variable and generally excessive relative to patient needs.1–4 Together 
with the fact that larger opioid prescriptions can increase the risks of long-term use, 
overdose, and diversion, these issues underscore postoperative prescribing as an 
important area for optimization.5–12 
 
There have been many efforts to try to improve opioid prescribing after surgery. Several 
sets of procedure-specific guidelines related to postoperative opioid prescribing have 
been published,13,14 but adoption may be limited. Best practices also include multimodal 
pain control, both to reduce the amount of opioid medications used and to mitigate 
opioid-related side effects.15 
 
Moreover, it has been shown from other healthcare contexts that behavioral “nudges” 
including electronic medical record (EMR) defaults, peer comparison feedback, and 
other behavioral interventions at a health systems level may be able to improve 
medication prescribing,16–18 but it is not known how best to apply these to postoperative 
care and opioid prescribing. Additionally, the potential impact, as well as any potential 
unintended effects, of such interventions aimed at discharge medication prescribing on 
subsequent healthcare utilization during the postoperative period is not known. 
 
 
3.0 Study Aims 
 
Aim 1. Assess the feasibility of using provider-level randomization to study the impact of 
a behaviorally designed, EMR-based discharge medication intervention. 
 
Aim 2. Evaluate the impact of this intervention, compared to usual care, on opioid 
prescribing after common surgical operations. 
 
Aim 3. Evaluate the impact of this intervention, compared to usual care, on healthcare 
utilization outcomes after common surgical operations. 
 
We hypothesize that the amount of opioids prescribed at discharge postoperatively will 
be lower in the intervention arm, and that any additional opioid prescribing and 
healthcare utilization in the following 30 days after discharge will not be significantly 
higher in the intervention arm. 
 
 
 

 
6 
4.0 Design and Methods 
 
Study design: This trial is designed as a single-center, cluster-randomized, pragmatic 
superiority trial, with two parallel groups and a primary outcome of the opioid amount 
prescribed to patients at discharge after surgery. Randomization will be performed at 
the provider level with 1:1 randomization, and analysis will be conducted at the patient-
encounter level. 
 
Surgical providers will be randomized at the start of the study to either the intervention 
arm, in which they see the novel version of discharge order sets in the EMR, or the 
usual care arm, in which they see standard versions of these order sets. Patients 
eligible for the study (as detailed below) and who present for surgery during the study 
period will undergo the regular processes of clinical care, and ultimately be discharged 
by a provider who may be in the intervention or the usual care study arm. Data 
abstracted from the EMR will be used to compare outcomes between study arms. 
 
Study setting: The study will be conducted within a single academic medical center, the 
University of Washington (Seattle, WA, USA), encompassing three physical locations: 
the University of Washington-Montlake, the University of Washington-Northwest, and 
Harborview Medical Center. 
 
Eligibility criteria:  
Inclusion criteria: 
• Age ≥ 18 years 
• Undergo one of these surgical operations during the index encounter: 
o Laparoscopic appendectomy 
o Laparoscopic cholecystectomy 
o Laparoscopic Nissen fundoplication 
o Laparoscopic sleeve gastrectomy 
o Laparoscopic Roux-en-Y gastric bypass 
o Laparoscopic or robotic colectomy 
o Open colectomy 
o Open small bowel resection or lysis or adhesions 
o Thyroidectomy 
o Parathyroidectomy 
o Open ventral hernia repair 
• Discharge by a surgical provider included in the study (randomization) 
 
Exclusion criteria: 
• Multiple operations during the index encounter 
• Death prior to discharge 
 
 
 

 
7 
4.1 Intervention 
 
A novel discharge medications section will be placed into two existing order sets in the 
EMR commonly used by surgical providers for discharge orders. This includes bundles 
of medications with procedure-specific, pre-selected quantities and strengths of opioids, 
non-opioid analgesics, and other adjunctive medications to help manage nausea and 
opioid-related adverse effects for common surgical operations (Table 1). For each 
procedure type, the orders reflect evidence-based guidelines for opioid prescribing after 
each specific procedure,13 recommendations for multi-modal postoperative pain 
management,15 and expert consultation with providers at the study site. 
 
In practice, providers in the intervention arm will be able to select the relevant procedure 
for a given patient encounter, and the corresponding medications would pre-populate as 
discharge prescriptions (Figure 1). All individual orders remain editable by providers, 
and providers can also order medications outside of the order sets as in the usual care 
situation. 
 
Table 1. Discharge medications by surgical procedure in the intervention  
Procedure 
Discharge Medications 
Laparoscopic 
appendectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #60 
[x] Ibuprofen 400mg tablet, 400mg q6h, #30 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #8 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #20 
 
Laparoscopic 
cholecystectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #60 
[x] Ibuprofen 400mg tablet, 400mg q6h, #30 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #8 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #20 
 
Laparoscopic 
Nissen 
fundoplication 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #60 
[x] Ibuprofen 400mg tablet, 400mg q6h, #30 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #10 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #20 
 
Laparoscopic 
sleeve 
gastrectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, crush, #60 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, crush, #10 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #15 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Omeprazole 20mg tablet, 20mg daily, crush, #30 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, crush, #20 
 
Laparoscopic 
Roux-en-Y gastric 
bypass 
[x] Acetaminophen 500mg tablet, 1000mg q6h, crush, #60 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, crush, #10 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #15 

 
8 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Omeprazole 20mg tablet, 20mg daily, crush, #90 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, crush, #20 
 
Laparoscopic or 
robotic colectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #60 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #10 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
 
Open colectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #80 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #15 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
 
Open small bowel 
resection or lysis 
or adhesions 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #80 
[x] Ibuprofen 400mg tablet, 400mg q6h, #40 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #15 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #20 
 
Thyroidectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #50 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #5 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #10 
 
Parathyroidectomy 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #50 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #5 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #10 
 
Open ventral 
hernia repair 
[x] Acetaminophen 500mg tablet, 1000mg q6h, #60 
[x] Ibuprofen 400mg tablet, 400mg q6h, #30 
[x] Oxycodone 5mg tablet, 5mg q6h PRN pain, #10 
[x] Ondansetron 4mg ODT, 4mg q8h PRN nausea, #10 
[x] Polyethylene glycol 17g powder, 17g daily, hold for loose stool, #255g 
[x] Senna 8.6mg tablet, 8.6mg daily PRN constipation, #20 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
9 
Figure 1. Mockup of intervention in order sets seen by providers 
 
 
 
 
 
 
 
4.2 Outcomes 
 
Primary outcome:  
Difference between the two study arms in mean total opioid amount, measured in 
morphine milligram equivalents, in the postoperative discharge prescription for 
the index surgical encounter. 
 
Secondary outcomes: 
• Difference between the two study arms in the mean total amount of any 
additional opioids prescribed, measured in morphine milligram equivalents, after 
and within 30 days of discharge from the index encounter. 
• Difference between the two study arms in the proportion with a documented 
phone call to surgical clinics or services at the study sites after and within 30 
days of discharge from the index encounter. 
• Difference between the two study arms in the proportion with an emergency 
department visit at the study sites after and within 30 days of discharge from the 
index encounter. 

 
10 
• Difference between the two study arms in the proportion with a readmission to a 
surgical service at the study sites after and within 30 days of discharge from the 
index encounter. 
 
 
4.3 Study Schedule 
 
Providers will be randomized just prior to study initiation. During the study, eligible 
patients (patient-encounter events) will undergo their usual operative and perioperative 
care. They will be identified subsequently via querying the EMR, with outcomes defined 
at baseline – the time of discharge from the index encounter – and during the follow-up 
period – after and within 30 days of discharge as specified above – for each patient-
encounter (Table 2). 
 
Table 2. Study Timepoints 
Item 
Baseline 
Follow-up  
(after and within 30 
days of discharge)  
Data source: EMR, to be queried monthly during the study period 
Demographic information 
x 
 
Clinical characteristics 
x 
 
Primary outcome 
x 
 
Secondary outcomes 
 
x 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
11 
5.0 Data Management and Information Security  
 
Data management will be the responsibility of the study investigators. Hospital IT will 
assist with data abstraction from the hospital EMR based on pre-specified data 
collection fields from the study investigators. IT will create an electronic study database 
to be stored on a dedicated, secure, HIPAA-compliant, institutional Microsoft Teams 
group with access limited to the study team. 
 
Data quality review will be conducted by the study investigators, including quality 
assurance checks during the study database creation process. The review will include 
enrollment, reasons for exclusion, patient demographics, and discharge medication 
prescription identification and categorization. 
 

 
12 
6.0 Statistical Analysis Plan 
 
 
6.1 Randomization Overview 
 
This is a single-center, cluster-randomized superiority trial, with two parallel groups. 
Randomization will be performed at the provider level, and analysis will be conducted at 
the patient-encounter level. 
 
Surgical providers including resident physicians and advanced practice providers at the 
study sites will be randomly assigned to either the control or intervention group with a 
1:1 simple randomization as per a computerized random number generator. 
 
For those providers allocated to the intervention arm, hospital IT will alter their EMR 
interface such that the intervention version of the discharge ordersets would be 
displayed to them. For those providers allocated to the control arm, the usual care 
version of the ordersets would be displayed. During data collection and analysis, those 
patient-encounters with discharge orders written by providers in the intervention arm will 
be classified as the intervention group, and those with discharge orders written by 
providers in the control arm will be classified as the control group. 
 
Due to the nature of the intervention and study design, neither clinical providers nor 
study staff can be blinded to allocation. 
 
 
6.2 General Analytic Strategy 
 
The primary analysis will compare the EMR intervention to usual care, aiming to assess 
whether the opioid amount prescribed at postoperative discharge at the index encounter 
is significantly different. The analytic strategy aims to account for the cluster-
randomized design and potential clustering of opioid prescribing by providers. 
 
1. Primary analysis, primary outcome – 
The total opioid amount, measured in morphine milligram equivalents, in the 
postoperative discharge prescription for the index encounter will be assessed 
using Generalized Estimating Equations (GEE) with a linear mean model and an 
exchangeable correlation structure, where the main exposure is an indicator of 
randomized treatment group assignment, adjusting for surgical procedure type 
out of consideration that this may be correlated with providers and with opioid 
amount prescribed at discharge. Cluster robust standard errors, clustered at the 
provider level, will be used. Statistical tests will be two-sided with alpha = 0.05. 
 
2. Secondary analyses 

 
13 
a. Secondary outcome 1 – The total amount of any additional opioids 
prescribed, measured in morphine milligram equivalents, after and within 
30 days of discharge from the index encounter will be similarly assessed 
using GEE with a linear mean model and an exchangeable correlation 
structure, where the main exposure is an indicator of randomized 
treatment group assignment, adjusting for surgical procedure type, with 
cluster robust standard errors, clustered at the provider level.  
b. Secondary outcome 2 – The proportion of patient-encounters with a 
documented phone call to surgical clinics or services after and within 30 
days of discharge from the index encounter will be assessed using GEE 
with a logistic model and an exchangeable correlation structure, where the 
main exposure is an indicator of randomized treatment group assignment, 
adjusting for surgical procedure type, with cluster robust standard errors, 
clustered at the provider level.  
c. Secondary outcome 3 – The proportion of patient-encounters with an 
emergency department visit after and within 30 days of discharge from the 
index encounter will be assessed using GEE with a logistic model and an 
exchangeable correlation structure, where the main exposure is an 
indicator of randomized treatment group assignment, adjusting for surgical 
procedure type, with cluster robust standard errors, clustered at the 
provider level.  
d. Secondary outcome 4 – The proportion of patient-encounters with a 
readmission to a surgical service after and within 30 days of discharge 
from the index encounter will be assessed using GEE with a logistic model 
and an exchangeable correlation structure, where the main exposure is an 
indicator of randomized treatment group assignment, adjusting for surgical 
procedure type, with cluster robust standard errors, clustered at the 
provider level. 
 
 
6.3 Sample Size and Statistical Power 
 
Using historic data from 2016-2020 from the study sites, estimations of statistical power 
and sample size were conducted.  
 
From these data, postoperative opioid prescriptions at discharge for the included 
surgical procedure types in this trial had a baseline mean opioid MME of 171. There are 
118 surgical providers who commonly discharge patients after these procedures, and 
an estimated surgical volume of 485 cases total of the included procedure types in a 6-
month period.  
 
 
 
 
 

 
14 
Numeric Results for a Test of Mean Difference 
──────────────────────────────────────── 
Test Statistic: T-Test with DF based on number of subjects 
Hypotheses: 
H0: δ = 0   vs.   Ha: δ ≠ 0 
 
 
 
Subj 
Subj 
Clus 
Clus 
Clus 
Clus COV 
 
 
 
 
 
Cnt 
Cnt 
Cnt 
Cnt 
Size 
Size Clus 
Diff 
Std 
 
 
 
Gr 1 
Gr 2 
Gr 1 
Gr 2 
Gr 1 
Gr 2 Sizes μ1-μ2 
Dev 
ICC 
 
Power 
N1 
N2 
K1 
K2 
M1 
M2 COV 
δ 
σ 
ρ 
Alpha 
0.20498 
242 
242 
59 
59 
4.1 
4.1 
0.53 
0.10 
0.66 
0.33 
0.05 
0.39709 
242 
242 
59 
59 
4.1 
4.1 
0.53 
0.15 
0.66 
0.33 
0.05 
0.61984 
242 
242 
59 
59 
4.1 
4.1 
0.53 
0.20 
0.66 
0.33 
0.05 
0.80819 
242 
242 
59 
59 
4.1 
4.1 
0.53 
0.25 
0.66 
0.33 
0.05 
0.92471 
242 
242 
59 
59 
4.1 
4.1 
0.53 
0.30 
0.66 
0.33 
0.05 
 
 
Report Definitions 
Power is the probability of rejecting a false null hypothesis. It should be close to one. 
N1 and N2 are the number of subjects in groups 1 and 2, respectively. 
K1 and K2 are the number of clusters in groups 1 and 2, respectively. 
M1 and M2 are the average number of items (subjects) per cluster in groups 1 and 2, respectively. 
COV is the coefficient of variation of the cluster sizes. 
δ is the mean difference (μ1 - μ2) in the response at which the power is calculated.  
σ is the standard deviation of the subject responses.  
ρ (ICC) is the intracluster correlation. 
Alpha is the probability of rejecting a true null hypothesis, that is, rejecting when the means are actually 
equal. 
 
The differences (δ) denote differences in log(MME) between providers exposed/not exposed to the 
intervention. The mean of log(MME) in the provided data was 4.91, with SD 0.66. These log-scale 
differences correspond to the following percentage changes in the geometric mean of MME on the linear 
scale. For example, a change from 4.91 to 4.81 (0.10) on the log scale would correspond to a change 
from 136 to 122.4 (10%) in the geometric mean of MME on the linear scale.  
   
δ (difference in log MME) 
% change in geometric mean MME 
0.10 
10% 
0.15 
14% 
0.20 
18% 
0.25 
22% 
0.30 
26% 
 
 
Summary Statements 
────────────────────────────────────────────── 
Sample sizes of 242 in group one and 242 in group two, which were obtained by sampling 59 clusters 
with an average of 4.1 subjects each in group one and 59 clusters with an average of 4.1 subjects each in 
group two, achieve 20% power to detect a difference between the group means of at least 0. The 
standard deviation of subjects is 0.66. The intracluster correlation coefficient is 0.33. The coefficient of 
variation of cluster sizes is 0.53. A two-sided t-test was used with a significance level of 0.05. This test 
used degrees of freedom based on the number of subjects. 
 
 
 

 
15 
7.0 Data and Safety Monitoring Plan (DSMP) and Study-Related SAEs and AEs 
 
This study is low risk. While all EMR features and ordersets can potentially influence 
clinical decisions, these risks can be mitigated by clinical judgment and discretion by 
providers who remain fully able to not use or modify any suggested medication orders 
as part of the study. Both the need to know evidence-based recommendations and the 
need to modify from them when appropriate are existing risks in usual clinical care.  
 
The primary risks of this study pertain to data confidentiality, to be addressed as below.  
Data confidentiality and security will be managed as delineated in section 5.0 Data 
Management and Information Security, including all existing institutional protections for 
electronic data security, and data storage only on a dedicated, secure, HIPAA-
compliant, institutional Microsoft Teams group with access limited to the study team. 
 
Oversight Responsibilities 
Oversight of the trial will be provided by the study investigators. 
 
Monitoring Procedures 
Study data are accessible at all times for the study investigators to review. The 
investigators will review AEs and SAEs on a quarterly basis. The lead investigator 
ensures all protocol deviations, AEs, and SAEs are reported to the steering committee 
and institutional review board according to the applicable regulatory requirements. 
 
Collection and Reporting of SAEs and AEs 
For this study, the following AE definitions are used: 
 
Adverse event:  
• Breach of confidentiality within the study data 
 
Serious adverse event:   
• Adverse event involving patients’ protected health information or providers’ 
prescribing records within the study data, presenting risks to patients and 
providers   
 
AEs are graded according to the following scale: 
 
Mild:  An experience that is transient, & requires no special treatment or 
intervention.  The experience does not generally interfere with usual daily 
activities.  This includes transient laboratory test alterations. 
 
Moderate:  An experience that is alleviated with simple therapeutic treatments.  
The experience impacts usual daily activities.  Includes laboratory test alterations 
indicating injury, but without long-term risk. 
 

 
16 
Severe:  An experience that requires therapeutic intervention.  The experience 
interrupts usual daily activities. If hospitalization (or prolongation of 
hospitalization) is required for treatment it becomes an SAE. 
 
The study uses the following AE attribution scale: 
 
Not related:  The AE is clearly not related to the study procedures (i.e., another 
cause of the event is most plausible and/or a clinically plausible temporal 
sequence is inconsistent with the onset of the event).   
 
Possibly related:  An event that follows a reasonable temporal sequence from the 
initiation of study procedures, but that could readily have been produced by a 
number of other factors. 
 
 
Related:  The AE is clearly related to the study procedures.   
 
AEs relating to data confidentiality for the study data are assessed through existing 
monitoring measures by hospital IT.  
 
 

 
17 
8.0 Protection of Human Participants 
 
The risks and benefits for participants involved in this trial are appropriately balanced. 
All of the medications in the intervention orderset are recommendations based on 
evidence from literature and on current best practices within the institution. To further 
mitigate the potential risks to patients and providers, text will also be included with the 
intervention orderset to emphasize to providers the potential need to modify orders and 
to use their clinical judgment. Both the need to know evidence-based recommendations 
and the need to modify from them when appropriate are existing risks in usual clinical 
care. We think we can potentially reduce risks to patients and providers by distilling 
these issues into a single EMR feature with medication defaults and accompanying 
information. 
 
Additionally, there is the potential risk of unauthorized access to patients’ protected 
health information, including medical and surgical records as well as information on 
opioid and other prescription medication use. A confidentiality breach could also cause 
potential harm to providers’ reputation and professional and financial standing. We will 
be taking appropriate and rigorous data security measures as described above.  
 
Based on the study design and the low-risk nature of the study, consent was waived by 
the institutional review board for providers and patients. The waiver of consent will allow 
for minimization of the potential for bias in the results from individuals acting differently 
in either study arm because they know they are being observed (Hawthorne effect). 
 
Participant confidentiality is strictly held in trust by the participating investigators and 
their staff. 
 
 
 
 
 
 
 

 
18 
9.0 References 
 
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3.  
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4.  
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Brummett CM, Waljee JF, Goesling J, et al. New persistent opioid use after minor 
and major surgical procedures in us adults. JAMA Surg. 2017;152(6). 
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6.  
Xu Y, Cuthbert CA, Karim S, et al. Associations Between Physician Prescribing 
Behavior and Persistent Postoperative Opioid Use Among Cancer Patients 
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7.  
Brescia AA, Waljee JF, Hu HM, et al. Impact of Prescribing on New Persistent 
Opioid Use After Cardiothoracic Surgery. Ann Thorac Surg. 2019;108(4):1107-
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8.  
Chua KP, Brummett CM, Conti RM, Bohnert A. Association of Opioid Prescribing 
Patterns with Prescription Opioid Overdose in Adolescents and Young Adults. 
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9.  
Schirle L, Stone AL, Morris MC, et al. Leftover opioids following adult surgical 
procedures: a systematic review and  meta-analysis. Syst Rev. 2020;9(1):139. 
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11.  Brat GA, Agniel D, Beam A, et al. Postsurgical prescriptions for opioid naive 
patients and association with overdose and misuse: Retrospective cohort study. 
BMJ. 2018;360. doi:10.1136/bmj.j5790 
12.  Powell D, Pacula RL, Taylor E. How increasing medical access to opioids 
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Econ. 2020;71:102286. doi:10.1016/j.jhealeco.2019.102286 
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