Optimization of Opioid Discharge Prescriptions Following Thyroid and Parathyroid Surgeries
Phase: NA
Conditions: Thyroidectomy, Parathyroidectomy, Opioid Use, Pain, Postoperative
Sponsor: The Methodist Hospital Research Institute

Official title: Optimization of opioid discharge prescriptions following 
thyroid and parathyroid surgeries  
 
NCT#: NCT04955444    
 
Document date: October 19, 2020 
 

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PROTOCOL TITLE: 
Optimization of opioid discharge prescriptions following thyroid and parathyroid surgeries 
Short title: Opioid use after thyroid and parathyroid surgeries 
 
PRINCIPAL INVESTIGATOR:  
Joshua T. Swan, PharmD, MPH, FCCM, BCPS 
Associate Professor of Pharmacy in Surgery and Outcomes Research 
Administrative Specialist in Research, Department of Pharmacy 
Director, Clinical Pharmacy Fellowship in Outcomes Research 
Houston Methodist Hospital  
Office: 713-441-0165 
Email: jtswan@houstonmethodist.org 
 
CO-INVESTIGATORS/ COLLABORATORS: 
Houston Methodist Co-Investigators 
Elsie Rizk, PharmD; HMH; Department of Pharmacy Research 
Tomona Iso, PharmD; HMH; Department of Pharmacy Research  
Fangzheng Yuan, PharmD; HMH; Department of Pharmacy Research 
Anh Thu Tran, PharmD; HMH; Department of Pharmacy Research 
Dipendra Aryal, MPharm, PhD; Department of Pharmacy Research  
Feibi Zheng, MD; HMH; Department of Surgery 
Ezekiel Fink, MD; HMH; Department of Neurology 
Linda Moore, RD; PhD; HMH; Department of Surgery 
Ahmed Gaber, MD; HMH; Department of Surgery 
Additional staff are listed in the MORTI IRB application. 
 
University of Houston Collaborators 
Matthew Wanat, PharmD; University of Houston; College of Pharmacy  
J. Douglas Thornton, PhD, PharmD; University of Houston; College of Pharmacy 
 
SPONSOR / FUNDING AGENCY  
Sponsor: Houston Methodist, Houston, Texas, USA 
Funding agency: This study will be funded by the University of Houston, College of Pharmacy, 
Department of Pharmaceutical Health Outcomes and Policy as a sub-award from a grant received 
from the Texas Health and Human Services Commission. 
 
VERSION NUMBER/DATE  
Protocol Version 2 / October 19, 2020 
 
 
 
 

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REVISION HISTORY 
Revision # 
Version Date 
Summary of Changes 
Consent Change (Yes/No) 
1 
October 19, 2020 
1. Increase the sample size 
of the post-implementation 
group from 75 to 80 patients 
 
2. Update the study timeline  
Not applicable 
 
1. Study Summary  
NA 
2. Purpose of the Study / Objectives  
Objective 
The overall objectives of this study are to build, implement, and measure the impact of a quality 
improvement bundle to increase the proportion of thyroidectomy and parathyroidectomy 
postoperative patients who are discharged without new opioid prescriptions.  
 
Research aims 
Aim 1. Build the quality improvement bundle 
Through cooperation of the Opioid Stewardship Program, information technology department, 
and surgical medical team, our research team will deploy a quality improvement bundle that will 
include (1) patient education, (2) provider education, and (3) electronic health record (EHR) 
configuration.   
Aim 1a. Educate patients 
Educational material will be adapted or newly developed and provided to patients prior to surgery 
(e.g., 1-page educational flyer) to set realistic pain and function goals and to discuss the role of 
opioids in the postoperative pain management plan. Additional education material will be prepared 
and given to patients at hospital discharge to counsel them on their pain management regimen 
and to emphasize the importance of non-opioid analgesia and non-pharmacological pain 
management strategies following hospital discharge. Patient educational materials will be 
delivered as part of routine medical care.  
Aim 1b. Educate providers  
Since most discharge opioid prescriptions are written by surgical residents or advanced practice 
practitioners, the research team will provide education to establish best practices for opioid 
discharge prescribing. Using a collaborative approach, the research team will provide in-service 
presentations and design and deploy pocket guides and wall flyers that remind providers of best 
practices during discharge planning activities.  
Aim 1c. Enhance EHR support via new thyroid/parathyroid surgery perioperative and 
discharge order sets 
Non-opioid analgesia options will be displayed to providers through perioperative order sets.  
Example of a perioperative multimodal analgesia (MMA) regimen 1,2  
1. Approximately one hour before procedure initiation: acetaminophen 1000 mg PO, 
gabapentin 100-300 mg PO, and meloxicam 7.5 mg PO 

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2. Standard of care intraoperative anesthesia: intravenous propofol, midazolam, and fentanyl 
+ inhaled anesthetics + antiemetics (intravenous dexamethasone and ondansetron) + 
subcutaneous infiltration with 1% lidocaine, with 1:100000 epinephrine solution, or 0.25% 
bupivacaine with 1:200 000 epinephrine solution  
3. Following resumption of oral intake after the procedure: ibuprofen 600 mg PO and 
acetaminophen 500 mg PO every 6 hours, on an alternate staggered schedule (1 agent 
every 3 hours) for the initial 48 hours and subsequently as needed for discomfort. 
To help providers identify opportunities to discharge patients without opioid prescriptions, the 
study team will collaborate with key provider stakeholders to design medical record alerts or 
guidance text that will appear to the provider during discharge medication reconciliation to identify 
patients who are candidates for being discharged without opioid prescriptions. A thyroid and 
parathyroid surgery discharge pathway/order set will be developed with defaults to non-opioid 
analgesics first. If opioids are selected as a second-line option, the medical record will default to 
short durations and low doses.  
Examples of first line non-opioid analgesic orders include (order set defaults): 
• 
Acetaminophen (TYLENOL) 325 mg PO Q4H-6H PRN for mild pain for up to 7 days  
• 
Acetaminophen (TYLENOL) 500 mg PO Q6H PRN for moderate pain for up to 7 days 
• 
Ibuprofen 200 to 600 mg PO Q6H PRN for moderate pain for up to 7 days 
• 
Naproxen sodium (ALEVE) 220 mg PO Q24H PRN for moderate pain for up to 7 days  
Examples of second line opioid analgesics include:  
• 
Tramadol 50 mg PO Q6H PRN for moderate pain for up to 5 days (#10) 
• 
Acetaminophen-codeine (TYLENOL WITH CODEINE #3) 300-30 mg Q6H PRN for 
moderate pain for up to 5 days (#10) 
Aim 2. Implement the quality improvement bundle 
The team will secure necessary hospital committee approvals that are routinely used to clinically 
validate and approve EHR enhancements and educational materials. Perioperative EHR order 
sets will be built by Houston Methodist information technology analysts and will be validated by 
the team of clinical experts supporting this project prior to implementation. The team will carefully 
select a go-live date using strategies that are customary for quality improvement implementations 
at Houston Methodist Hospital. The educational materials for patients will be implemented on a 
single date in all surgeon clinics and within the discharge process workflow. Provider educational 
material will be handed out and displayed in surgery floors, and multiple educational lectures will 
be held for different teams and shifts within a 1-month period.  
Aim 3. Measure impact of the bundle 
Following complete implementation of all components of the bundle from Aim 2 (planned for 
quarter 4 of 2020 and quarter 1 of 2021), investigators will start to collect post-implementation 
data and will measure the proportion of thyroidectomy and parathyroidectomy postoperative 
patients who do not receive new opioid prescriptions at discharge. The post implementation data 
(quarters 2 and 3 of 2021) will be compared to the historical (baseline) data obtained from January 
2018 to December 2019.  
 
 
 
 

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Endpoints 
Primary endpoint 
The primary endpoint will be the proportion of thyroidectomy and parathyroidectomy postoperative 
patients who do not receive new opioid prescriptions at discharge. This outcome excludes 
continuation of previous, chronic opioid therapy. 
Secondary endpoints 
• Proportion of thyroidectomy and parathyroidectomy patients with opioid discharge prescriptions 
exceeding the recommended dose of 112.5 oral MMEs3 
• Proportion of postoperative patients with opioid discharge prescriptions exceeding 5 days 
• Proportion of thyroidectomy and parathyroidectomy postoperative patients who do not receive 
new opioid prescriptions from the surgeon’s office within 7 days of surgery including discharge 
prescriptions. This outcome excludes continuation of previous, chronic opioid therapy 
• Proportion of postoperative patients with opioid prescriptions exceeding 50 oral MMEs/day when 
new postoperative discharge prescriptions are added to existing opioid therapy 
 
Research question  
Does the implementation of a quality improvement bundle including patient education, provider 
education, and EHR support increase the proportion of thyroidectomy and parathyroidectomy 
postoperative patients who are discharged without new opioid prescriptions?  
 
3. Background 
Opioid discharge prescriptions predict long-term opioid use 
Approximately 99% of postoperative patients receive opioid analgesics, and 14% experience 
opioid-related adverse effects, which increase hospitalization costs, length of stay, and 30-day 
hospital readmission.4-6 Acute pain management with opioids following discharge from the 
hospital or emergency department is a determinant for chronic opioid use, dependence, and 
overdose.7,8 Approximately 6% of patients persistently use new opioid therapy at 90 to 180 days 
after major or minor surgery. The incidence of new, persistent opioid use was 5% after 
thyroidectomy and 6% after parathyroidectomy procedures.9 
Optimal opioid discharge prescriptions after surgery 
For general surgery, the optimal duration of initial opioid discharge prescriptions is 4 to 9 days. 10 
Patients undergoing thyroid and parathyroid surgeries can be discharged from the hospital with 
prescriptions as low as 20 to 70 oral morphine milligram equivalents (MMEs), and approximately 
93% of patients will consume less than 100 oral MMEs within 2 weeks following hospital 
discharge.11,12 Recently, a multidisciplinary expert panel with patient engagement published 
surgery specific recommendations suggesting less than 112.5 oral MMEs for opioid discharge 
prescriptions following thyroidectomy procedures.3 However, up to 33% to 58% of patients 
undergoing thyroid and parathyroid surgeries do not need any opioid discharge prescriptions 
following surgery.11 Quality programs that optimized perioperative MMA regimens, provider 
education, shared decision making, and patient education and empowerment have successfully 
reduced the proportion of patients discharged with opioid prescriptions to less than 5%. 1,13 
In 2018, our team developed a list of quality indicators for the Houston Methodist Opioid 
Stewardship Program.14 Six quality indicators focused on appropriate patient discharge on an 
adequate acute pain management regimen: 
• 
Proportion of opioid discharge prescriptions with a duration greater than 7 days 
• 
Proportion of discharged patients with opioid discharge prescriptions ≥ 50 MME per day 

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• 
Proportion of patients discharged on opioids who received education on opioid purpose, 
adverse effects, monitoring, secure storage and disposal, and alternatives  
• 
Proportion of patients discharged from the hospital with opioid discharge prescriptions  
• 
Proportion of patients with opioid discharge prescriptions given in the emergency 
department that exceed 3 to 5 days  
• 
Proportion of patients discharged from the emergency department with opioid discharge 
prescriptions   
This quality improvement study directly addresses these quality indicators and aims to optimize 
opioid discharge prescriptions after thyroid and parathyroid surgery.  
4. Study Design 
Overview 
This quality improvement, quasi-experimental study will be conducted in Houston Methodist 
Hospital to compare opioid discharge prescribing practices before and after the implementation 
of the quality improvement bundle that is expected to occur in quarter 4 of 2020 and quarter 1 of 
2021. The pre-implementation (baseline) data on patients treated in 2018 and 2019 will be 
collected by the study team. 
Patient population 
The historical control data will include 160 patients who underwent a thyroidectomy or a 
parathyroidectomy procedure from January 2018 to December 2019. Twenty patients will be 
randomly sampled from each of the 8 quarters during the baseline period to provide historical 
trend leading up to the implementation. The post-implementation group will consist of the first 80 
qualifying patients accrued over a 4- to 6-month period following the deployed bundle. Patients 
who underwent a thyroidectomy or parathyroidectomy procedure will be retrospectively identified 
using the ACS National Surgical Quality Improvement Program (ACS NSQIP®) data base and 
data reports from the Houston Methodist information technology department.   
Study procedure 
Table 1. Study procedure 
Development 
• Team recruitment 
• Protocol development 
• EHR enhancements development 
• Educational documents development 
Approval  
• IRB approval 
• Relevant patient care and quality committees 
Implementation  • EPIC information technology team engagement 
• Resident, nurse practitioner, and physician assistants education    
• Testing the EHR enhancements   
Evaluation  
• Data collection  
• Data analysis 
Results report 
• Manuscript preparation  
• Report data back to HM committees for stakeholder engagement and 
follow-up to optimize appropriate perioperative pain management 
regimens  
 
 

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Study timeline 
This study will start on January 1, 2020 and will end on Dec 31, 2021. Historical data collection 
will occur during quarters 1 to 2. Historical data will be analyzed and used to inform development 
of study interventions. Interventions will be developed and deployed during quarter 4 of 2020 and 
quarter 1 of 2021. Preliminary data estimates that approximately 300 thyroidectomy and 
parathyroidectomy surgeries are performed annually at Houston Methodist Hospital, which is 
approximately 25 per month. Following deployment, data on 80 post-implementation patients will 
be accrued over a 4- to 6-month period (quarters 2 and 3 of 2021). The analysis and final report 
will be completed and submitted to the University of Houston prior to December 31, 2021.  
Table 2. Estimated project timeline 
Activity 
2020 
2021 
Q1 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Historical data collection 
X 
X 
 
 
 
 
 
 
Develop bundle 
 
X 
X 
 
 
 
 
 
Implement bundle 
 
 
 
X 
X 
 
 
 
Post-implementation patient accrual 
 
 
 
 
 
X 
X 
 
Analysis 
 
 
 
 
 
 
X 
X 
Submit final report to University of 
Houston 
 
 
 
 
 
 
 
X 
 
Statistical analysis plan  
Primary analysis  
The primary outcome of this study is binary. The primary analysis will be a multiple variable logistic 
regression on this binary outcome that will be adjusted for candidate covariates. The list of 
candidate covariates includes: (1) pre-hospital opioid use (opioid naïve vs. non-naïve), (2) ASA 
score, (3) age, (4) surgery complexity, (5) surgery duration, (6) cancer diagnosis, (7) AUC of 
postoperative pain scores documented up to 12 hours prior to discharge, and (8) postoperative 
oral opioid MMEs (or number of opioid doses) administered up to 12 hours prior to hospital 
discharge. 
Sample size estimates  
The historical control data will include 160 patients who underwent a thyroidectomy or a 
parathyroidectomy procedure from January 2018 to December 2019. Twenty patients will be 
randomly sampled from each of the 8 quarters during the baseline period to provide historical 
trend leading up to the implementation. Using preliminary data, investigators estimate that 35% 
of thyroidectomy and parathyroidectomy patients were discharged without new opioid 
prescriptions in 2018 and 2019.  
The quality improvement bundle is expected to result in an absolute risk increase of 20% in the 
primary outcome. Table 3 shows estimated sample sizes that were calculated before pre-
implementation (baseline) data was collected and that were not adjusted for potential 
confounders. 
 
Table 3. Estimated samples sizes for post-implementation cohort to achieve 80% power 

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Estimated historical 
control proportion 
Absolute risk 
increase 
Estimated post-
implementation 
proportion 
Estimated post- 
implementation sample 
size 
35% 
20% 
55% 
68 
40% 
20% 
60% 
70 
45% 
20% 
65% 
69 
The estimates provided in the table above assumed a historical control sample size of 160 
patients and estimated samples sizes needed to obtain 80% power using a 2-sided alpha of 0.05  
 
After obtaining pre-implementation (baseline) data, investigators determined that the historical 
control proportion was 20%. Preliminary models were built to evaluate the effect of potential 
confounders on the proportion of patients who do not receive new opioid prescriptions at 
discharge. It was determined that 19% to 25% of the variability in the outcome was explained by 
the model (R2 of 0.19 to 0.25). To ensure adequate sample size, a conservative approach was 
used, and sample size was adjusted by the variance inflation factor (1/1-R2).15,16 For a historical 
group with a sample size of 160 and a baseline proportion of 20%, a post-implementation sample 
size of 80 patients would provide 80% power to detect an absolute risk increase of 20%. 
 
Statistical analyses will be conducted using STATA version 15 (StataCorp LP, College Station, 
Texas). All patient demographics and baseline characteristics will be summarized using 
descriptive statistics. Continuous variables will be reported using mean (and standard deviation) 
or median (and interquartile range). Categorical variables will be reported with counts and 
percentages. Confidence intervals at the 95% level will be calculated for proportions of patients 
with primary and secondary outcomes.  
 
Secondary analysis  
Trends of the primary outcome will be described over each quarter of the pre-implementation, 
control cohort starting January 2018 until December 2019. Monthly trends will also be described 
for the post-implementation cohort in quarters 2 and 3 of 2021.   
 
Primary endpoint 
The primary endpoint will be the proportion of thyroidectomy and parathyroidectomy postoperative 
patients who do not receive new opioid prescriptions at discharge. This outcome excludes 
continuation of previous, chronic opioid therapy. 
Secondary endpoints 
• Proportion of thyroidectomy and parathyroidectomy patients with opioid discharge prescriptions 
exceeding the recommended dose of 112.5 oral MMEs3 
• Proportion of postoperative patients with opioid discharge prescriptions exceeding 5 days 
• Proportion of thyroidectomy and parathyroidectomy postoperative patients who do not receive 
new opioid prescriptions from the surgeon’s office within 7 days of surgery including discharge 
prescriptions. This outcome excludes continuation of previous, chronic opioid therapy 
• Proportion of postoperative patients with opioid prescriptions exceeding 50 oral MMEs/day when 
new postoperative discharge prescriptions are added to existing opioid therapy 
 

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Data management  
Data sources and collection  
This study will rely on secondary data collection from the medical record. Investigators will request 
a NSQIP data report from the department of surgery and a report from the Houston Methodist 
information technology department containing a list of patients who had a thyroidectomy or 
parathyroidectomy procedure within the study time frame. Investigators will also request data 
reports for some data elements from the Information Technology department. Supplemental data 
elements will be collected from the Houston Methodist electronic medical record (EPIC) through 
manual chart review. In addition to data collection from EPIC, investigators ask for permission to 
collect data on opioid medications filled by patients after discharge. This data would be collected 
from the Texas Prescription Monitoring Program (PMP) that collects and monitors prescription 
data for all Schedule II, III, IV, and V Controlled Substances dispensed by a pharmacy in Texas 
or to a Texas resident from a pharmacy located in another state. 
In addition to securely storing study data on the HM secure intranet, investigators ask for 
permission for study data capture, storage, and retrieval on Houston Methodist’s secure and 
password protected REDCap instance (Holly Hall Datacenter). Data collection f orms may also be 
built in Microsoft Excel® or Microsoft Access® to collect and record data.  
Data elements  
Data elements that will be collected for this study are described in table 4.  
Table 4. Study data scope 
 
Variables 
Description 
Source 
Demographics 
Age in years 
[0 to 115] 
Age at surgery encounter  
IT data report 
Gender 
Male 
Female 
IT data report 
Height in inches 
[0 to 100] 
IT data report 
Weight in kg 
[0 to 500] 
IT data report 
Allergies 
 
EPIC chart review 
Race 
White/Caucasian 
Black/African American 
Asian 
Other 
IT data report 
Baseline assessments 
Surgery date 
 
IT data report 
NSQIP data base  
Surgery type 
Thyroid lobectomy 
Total thyroidectomy 
Unilateral parathyroidectomy 
Bilateral parathyroidectomy 
Both thyroidectomy and 
parathyroidectomy 
NSQIP data base 
EPIC chart review 
Surgery indication  
 
EPIC chart review  
Admission/discharge/transfer 
events (ADT) 
Dates and hospital locations  
IT data report 
EPIC chart review 
Encounter type 
Inpatient, outpatient, observation 
IT data report 
EPIC chart review 

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Variables 
Description 
Source 
ICD10 diagnosis and 
procedure codes  
 
IT data report 
  
Other surgeries performed 
during the same encounter  
 
IT data report 
EPIC chart review  
Comorbidities  
e.g., kidney disease, liver 
disease, cancer, chronic pan …  
IT data report 
EPIC chart review 
Home/prior to admission 
medications   
  
IT data report 
EPIC chart review 
ASA score 
 
IT data report 
EPIC chart review  
Patient protected health information 
Name 
 
IT data report 
MRN 
 
IT data report 
VisitID/EncounterID 
 
IT data report 
Date of birth 
 
IT data report 
Date/time of admission  
 
 
Surgery data variables  
Anesthesia events 
Induction, intubation, post-
anesthesia assessments  
IT data report 
EPIC chart review  
Surgery duration 
 
IT data report 
EPIC chart review 
Surgery-related data 
Surgery variables (techniques, 
complexity, incision length, 
intraoperative complications…) 
from OP notes and brief OP 
notes  
IT data report 
EPIC chart review 
Vitals and pain assessments throughout the hospital encounter (including OR) 
Vital signs  
Blood pressure, heart rate, 
respiratory rate…  
IT data report 
EPIC chart review 
Pain scores  
All scales documented: 
Numerical pain rating scale, 
FACES, CPOT, Wong-Baker…   
IT data report 
EPIC chart review 
Pain descriptors in 
flowsheets  
Location, onset, type, frequency, 
orientation…  
IT data report 
EPIC chart review 
Function assessments in 
flowsheets  
Effect of pain on daily activities  
IT data report 
EPIC chart review 
POSS scores 
 
IT data report 
EPIC chart review 
Medication data variables throughout the hospital encounter (including OR) 
All medication orders and 
administrations throughout 
the hospital encounter  
Medication name, dose, route, 
frequency, order start/end time, 
administration time, rate 
changes, order set/order panel   
(for IV medications, PO 
medications, continuous 
infusions, PCA, PCEA, inhaled 
medications, patches)  
IT data report 
EPIC chart review  
Discharge data elements  
Discharge medications  
New prescriptions and previous 
medications resumed on 
discharge (medication name, 
IT data report 
EPIC chart review  

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Variables 
Description 
Source 
dose, route, frequency, quantity, 
refills, duration of use)  
Authorizing provider type  
MD, DO, NP, PA 
IT data report 
EPIC chart review  
Discharge order set use  
 
IT data report 
EPIC chart review  
Medical team’s follow up plan Discharge summary notes 
IT data report 
EPIC chart review  
Patient communications with 
medical team via EPIC 
(emails, phone calls text…)  
Medication inquiries and other 
patient questions  
EPIC chart review 
Pain assessments and opioid 
use after surgery  
Follow up clinic visits 
documented in EPIC (1-3 weeks 
after surgery)  
EPIC chart review 
PMP 
5. Study Intervention  
No experimental intervention is being deployed by this study. 
This quality improvement bundle will be developed and deployed in accordance with standard 
practices for quality improvement at Houston Methodist. The bundle will provide education to 
providers and patients and will enhance the electronic health record to provide clinical decision 
support. After implementation, this quality improvement bundle will become the new standard of 
care.  
6. Drugs, Biologics, Devices  
N/A. This study is not intended to evaluate any FDA regulated drug, biologic, or device.  
 
7. Collaborative / Multi-site Research 
This study will be conducted at HMH only. All patients will receive care at HMH, and all patient 
data will be stored at HMH. Collaborators from the University of Houston will provide guidance 
on study design and analysis, but will not have access to patient-level data.  
8. Data Privacy / Confidentiality  
Houston Methodist policies for Protected Health Information will be followed, including all 
requirements for physical and electronic data security, use of encrypted devices, and HM 
password protected servers.  
Throughout the conduct of this study, all required data will be recorded on approved electronic 
data collection forms that have been specifically designed to record all data pertinent to this 
clinical investigation. Data will include one or more of the 18 PHI identifiers. All electronic data will 
be stored within the corporate network on a secure server. Only Houston Methodist Research 
Institute credentialed investigators listed under this IRB submission will have access to identifiable 
patient data. Any paper records generated from this study will be stored in a locked HM office of 
an HM investigator. 

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PHI of medical record number (MRN), hospital encounter number (visitID), and date of admission 
or surgery will be used by the investigators to locate the charts of eligible patients in the EHR 
system to conduct manual chart review. The unique identifiers of MRN and visitID will be used to 
link data sheets in the study database and conduct relational database analyses.  
The clinical investigator is responsible for maintaining adequate and accurate records as specified 
in Essential Documents for the Conduct of a Clinical Trial (section 8 of the ICH Guideline for Good 
Clinical Practice) to enable the conduct of the study data collection to be fully documented, and 
the study data to be subsequently verified. Upon study termination and after all analyses are 
complete, all records and data sets will be archived and destroyed in accordance with HM 
research policies.  
During the course of this study, Houston Methodist investigators will collaborate with external 
investigators from the funding agency (University of Houston, College of Pharmacy): 
1. J. Douglas Thornton, PhD, PharmD, BCPS, Assistant Professor of Pharmaceutical 
Health Outcomes and Policy 
2. Matthew Wanat, PharmD, BCPS, BCCCP, FCCM, Clinical Associate Professor of 
Pharmacy Practice 
When necessary, only de-identified and aggregated data reports will be shared with these 
external collaborators and with the funding agency to protect patients’ confidentiality. 
The funding agency will not have access to the study database, patient-level economic data, or 
protected health information. 
No audio/video recordings or photographs will be used during the course of this study.  
A list of all patients who underwent a thyroid or parathyroid surgery during the study time frame 
will be extracted from EPIC by HM Health Information Technology. Protected health information 
of name, MRN, CSN, date of birth, admission/discharge/surgery dates, and relevant drug 
administration dates will be collected. The PHI will be used by investigators to locate patient 
records in the EHR system to conduct manual chart review. The unique identifiers such as MRN, 
CSN and DOB will be used to link data sheets in the study database and conduct random chart 
audits for data quality assurance. 
 
Identifier (or parts of)  
Recorded  Disclosed  Comment 
Names 
Yes 
No 
Investigators 
use 
name 
during 
chart 
review 
to 
confirm that the correct 
patient’s chart was opened 
for data collection.  
All elements of dates (except year) for 
dates directly related to an individual, 
including birth date, admission date, 
discharge date, date of death; and all ages 
over 89 and elements of dates (including 
year) indicative of such age 
Yes 
No 
 
Medical record numbers 
Yes 
No 
 
 
Data for all patients who underwent a thyroid or parathyroid surgery during the study time frame 
will be extracted from EPIC by HM Health Information Technology. PHI of name, MRN, CSN, date 
of birth, admission/discharge/surgery dates, and relevant drug administration dates will be 
collected.  

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Houston Methodist policies for Protected Health Information will be followed, including all 
requirements for physical and electronic data security, use of encrypted devices, and HM 
password protected servers.  
Throughout the conduct of this study, all required data will be recorded on approved electronic 
data collection forms that have been specifically designed to record all data pertinent to this 
clinical investigation. Data will include one or more of the 18 PHI identifiers. All electronic data will 
be stored within the corporate network on a secure server. Only Houston Methodist Research 
Institute credentialed investigators listed under this IRB submission will have access to identifiable 
patient data. Any paper records generated from this study will be stored in a locked HM office of 
an HM investigator. 
The clinical investigator is responsible for maintaining adequate and accurate records as specified 
in Essential Documents for the Conduct of a Clinical Trial (section 8 of the ICH Guideline for Good 
Clinical Practice) to enable the conduct of the study data collection to be fully documented, and 
the study data to be subsequently verified. Upon study termination and after all analyses are 
complete, all records and data sets will be archived and destroyed in accordance with HM 
research policies.  
When necessary, only de-identified and aggregated data reports will be shared with external 
collaborators and with the funding agency to protect patients’ confidentiality. The funding agency 
will not have access to the study database, patient-level economic data, or protected health 
information. 
9. Data and Specimen Banking  
This retrospective study does not involve any specimen banking.  
10.Study Population  
N/A. This study only involves secondary data collection from the medical record.  
11.Screening and Recruitment 
N/A. This study only involves secondary data collection from the medical record.  
12. Withdrawal of Subjects 
N/A. This is a retrospective study will collect information from the medical record for patients 
who have been already treated.  
13. Provisions to Protect the Privacy Interests of Subjects  
Houston Methodist policies for Protected Health Information will be followed, including all 
requirements for physical and electronic data security, use of encrypted devices, and HM 
password protected servers.  
Throughout the conduct of this study, all required data will be recorded on approved electronic 
data collection forms that have been specifically designed to record all data pertinent to this 
clinical investigation. Data will include one or more of the 18 PHI identifiers. All electronic data will 
be stored within the corporate network on a secure server. Only Houston Methodist Research 
Institute credentialed investigators listed under this IRB submission will have access to identifiable 

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patient data. Any paper records generated from this study will be stored in a locked HM office of 
an HM investigator.  
14.Risks to Subjects 
The only risk to patients of this study is potential, unintended disclosure of protected health 
information. 
To prevent potential, unintended disclosure, investigators will secure health information and study 
data on password protected HM computers, on the password protected HM intranet servers, and 
in locked investigator offices at HMH. Only Houston Methodist Research Institute credentialed 
investigators listed in this IRB submission will have access to the data and records. De-identified, 
aggregated data reports may be shared with external investigators (faculty from the University of 
Houston, College of Pharmacy), if needed. 
15. Potential Benefits  
This retrospective study does not have any direct benefits as patients were treated with standard 
of care. However, this study is evaluating the impact of a quality improvement bundle that is 
expected to improve the safety of pain management after thyroid and parathyroid surgery and 
reduce opioids in the community, while maintaining optimal pain management. 
16. Financial and Economic Issues  
N/A 
17. Data Safety Plan  
This research involves secondary use of data only.  
18.Informed Consent Documentation and Process  
Investigators request a waiver of informed consent for this retrospective research that uses 
existing records.  
19. Waiver of Informed Consent and /or Authorization  
Investigators are requesting a waiver of informed consent for this project.   
 
1. The study is no more than minimal risk to the participants 
The only risk to patients of this study is potential, unintended disclosure of protected health 
information.  
To prevent potential, unintended disclosure, investigators will secure health information and 
study data on password protected HM computers, on the password protected HM intranet 
servers, and in locked investigator offices at HMH. Only Houston Methodist Research Institute 
credentialed investigators listed in this IRB submission will have access to the data and 
records. De-identified, aggregated data reports may be shared with external investigators 
(faculty from the University of Houston, College of Pharmacy), if needed. 
 
2. The research could not practicably be carried out without the requested waiver of 
consent 

Opioid use after thyroid and parathyroid surgeries 
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Investigators will collect data retrospectively. Patients would have been treated and left the 
hospital after surgery. Due to the retrospective nature, it may not feasible for investigators to 
contact patients to obtain consent. 
 
3. If the research involves using identifiable private information or identifiable 
biospecimens, the research could not practicably be carried out without using such 
information or biospecimens in an identifiable format 
PHI will be used by the investigators to locate the charts of eligible patients in the EHR system 
to conduct manual chart review. The unique identifiers of MRN and VisitID will be used to link 
data sheets in the study database. 
 
4. The waiver will not adversely affect the rights and welfare of the participants 
 
At the time of data collection for this study, patients would have been already discharged 
after receiving standard of care perioperative pain management.  
 
 
5. Whether participants will be provided with additional pertinent information after 
participation (i.e, whether debriefing will occur). 
Patients will not be informed that their data is being collected for this retrospective study. 
Debriefing is not applicable to this study. 
 
20.References 
 
1. 
Militsakh O, Lydiatt W, Lydiatt D, et al. Development of Multimodal Analgesia Pathways 
in Outpatient Thyroid and Parathyroid Surgery and Association With Postoperative 
Opioid Prescription Patterns. JAMA otolaryngology-- head & neck surgery. 
2018;144(11):1023-1029. 
2. 
Oltman J, Militsakh O, D'Agostino M, et al. Multimodal Analgesia in Outpatient Head and 
Neck Surgery: A Feasibility and Safety Study. JAMA otolaryngology-- head & neck 
surgery. 2017;143(12):1207-1212. 
3. 
Overton HN, Hanna MN, Bruhn WE, Hutfless S, Bicket MC, Makary MA. Opioid-
Prescribing Guidelines for Common Surgical Procedures: An Expert Panel Consensus. 
Journal of the American College of Surgeons. 2018;227(4):411-418. 
4. 
Oderda GM, Gan TJ, Johnson BH, Robinson SB. Effect of opioid-related adverse events 
on outcomes in selected surgical patients. J Pain Palliat Care Pharmacother. 
2013;27(1):62-70. 
5. 
Kessler ER, Shah M, Gruschkus SK, Raju A. Cost and quality implications of opioid-
based postsurgical pain control using administrative claims data from a large health 
system: opioid-related adverse events and their impact on clinical and economic 
outcomes. Pharmacotherapy. 2013;33(4):383-391. 
6. 
Minkowitz HS, Gruschkus SK, Shah M, Raju A. Adverse drug events among patients 
receiving postsurgical opioids in a large health system: risk factors and outcomes. Am J 
Health Syst Pharm. 2014;71(18):1556-1565. 
7. 
Jena AB, Goldman D, Karaca-Mandic P. Hospital Prescribing of Opioids to Medicare 
Beneficiaries. JAMA Intern Med. 2016;176(7):990-997. 

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8. 
Barnett ML, Olenski AR, Jena AB. Opioid-Prescribing Patterns of Emergency Physicians 
and Risk of Long-Term Use. N Engl J Med. 2017;376(7):663-673. 
9. 
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):e170504. 
10. 
Scully RE, Schoenfeld AJ, Jiang W, et al. Defining optimal length of opioid pain 
medication prescription after common surgical procedures. JAMA Surgery. 
2018;153(1):37-43. 
11. 
Shindo M, Lim J, Leon E, Moneta L, Li R, Quintinalla-Diek L. Opioid Prescribing Practice 
and Needs in Thyroid and Parathyroid Surgery. JAMA otolaryngology-- head & neck 
surgery. 2018. 
12. 
Lou I, Chennell TB, Schaefer SC, et al. Optimizing Outpatient Pain Management After 
Thyroid and Parathyroid Surgery: A Two-Institution Experience. Ann Surg Oncol. 
2017;24(7):1951-1957. 
13. 
Ruffolo LI, Jackson KM, Juviler P, et al. Narcotic Free Cervical Endocrine Surgery: A 
Shift in Paradigm. Ann Surg. 2019. 
14. 
Rizk E, Swan JT, Cheon O, et al. Quality indicators to measure the effect of opioid 
stewardship interventions in hospital and emergency department settings. Am J Health 
Syst Pharm. 2019;76(4):225-235. 
15. 
Hsieh FY, Bloch DA, Larsen MD. A simple method of sample size calculation for linear 
and logistic regression. Stat Med. 1998;17(14):1623-1634. 
16. 
Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. An overview of variance inflation factors 
for sample-size calculation. Eval Health Prof. 2003;26(3):239-257.