A Patient-Centered Intervention to Improve Opioid Safety
Phase: NA
Conditions: Opioid Safety, Prescription Opioid Misuse, Chronic Pain
Sponsor: VA Office of Research and Development

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Protocol 
 
Study Title:  A Patient-Centered Intervention to Improve Opioid Safety  
 
NCT02791399 
 
This protocol contains the original statistical analysis plan excerpted from the funded research 
proposal.  
04/24/2019 
 
Details of the final protocol and recruitment outcomes are in the following publication:  
Morasco, B.J., Adams, M.H., Maloy, P.E., Hooker, E.R., Iacocca, M.O., Krebs, E.E., Carr, T.P., 
Lovejoy, T.I., Saha, S., & Dobscha, S.K. (2020). Research methods and baseline findings of the 
Improving the Safety of Opioid Therapy (ISOT) cluster randomized trial. Contemporary Clinical Trials, 
90, 105957. 

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A Patient-Centered Intervention to Improve Opioid Safety 
 
SPECIFIC AIMS 
With the increased use of prescription opioid medications to treat chronic pain,1 there is growing 
and appropriate concern about impacts of their use. Prescription opioids are associated with 
increased risk of adverse medical effects, including cardiovascular events, fractures, 
hospitalization, and mortality.2-7 Up to two-thirds of patients prescribed opioids will exhibit some 
type of prescription opioid misuse, such as taking more medication than prescribed, obtaining 
early refills or medication from others, taking opioid for purposes other than analgesia, or 
sharing with friends or family.8,9 Prescription opioid abuse, which includes concurrent alcohol 
abuse or illicit substance use, occurs in 10-35% of patients with chronic pain.10 VA/DoD Clinical 
Practice Guidelines for patients prescribed chronic opioid therapy for pain recommend 
screening all patients with urine drug tests (UDT) for prescription opioid abuse and adherence 
prior to and during opioid therapy.11 Recent reports from the VA Office of Inspector General and 
Under Secretary for Health additionally highlight the need to be more thorough in screening for 
adherence to prescription opioids. However, UDTs are currently administered to fewer than 25% 
of patients who initiate chronic opioid therapy.12 Reasons for the limited use of UDTs may 
include clinician discomfort, uncertainty about interpreting or responding to results, and lack of 
clear data on effectiveness of screening to reduce prescription opioid abuse.13,14 
We propose a randomized trial of a multifaceted intervention designed to optimize a patient- 
centered approach for improving adherence to prescription opioids, “Improving the Safety of 
Opioid Prescriptions (ISOT).” Primary care clinicians and PACT (Patient Aligned Care Team) 
nurses will be randomized to ISOT or the control condition; patients will be nested by clinician 
status. Clinicians and PACT nurses in both groups will participate in an interactive workshop on 
reducing prescription opioid abuse and enhancing safety. In addition, clinicians assigned to 
ISOT will work with a nurse care manager (NCM) who will maintain a registry of enrolled 
patients, track UDT administrations and results, examine state Prescription Drug Monitoring 
Program data, monitor medical records, and collaborate with an internist and substance abuse 
specialist to provide decision support when patients demonstrate evidence of prescription opioid 
misuse or abuse. The NCM will also meet with intervention patients to discuss strategies for 
reducing opioid adverse effects and preventing diversion, and to provide rationale for 
prescription opioid adherence monitoring. Thus, our intervention targets system, clinician, and 
patient levels. A research goal is to enhance patient-centered pain care and improve the quality 
of the communication and relationship between the patient and the primary care team pertaining 
to this challenging topic. The main objectives of this study are to evaluate the efficacy and 
acceptability of a multifaceted intervention (ISOT) to enhance opioid safety in patients with 
chronic pain. 
SPECIFIC AIMS 
Aim 1: Evaluate to what extent a multifaceted intervention (ISOT) enhances prescription opioid 
safety. 
Hypothesis 1: At 12 months, ISOT patients will have lower rates of prescription opioid 
abuse, as assessed via multiple methods (self-report, clinical interview, urine drug testing), 
compared to patients of clinicians randomized to the control condition. 
Hypothesis 2: At 12 months, ISOT patients will experience fewer adverse events (defined in 
this proposal to include emergency room visits, overdoses, falls, motor vehicle accidents). 
Aim 2: Assess to what extent assignment to ISOT impacts pain treatment-related processes of 
care. 
Hypothesis 1: ISOT patients will have more modifications to clinical care documented in the 
medical record, including more frequent UDTs and queries to state prescription drug 

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monitoring programs. 
Hypothesis 2: ISOT patients will report greater trust and satisfaction with their PCP related 
to opioid prescribing and treatment of chronic pain, relative to patients in the control 
condition. 
Aim 3: Explore to what extent ISOT is associated with changes in pain intensity, function, 
quality of life, and depressive symptoms. 
Hypothesis: ISOT patients, relative to patients in the control condition, will not experience 
worse significant adverse outcomes in pain intensity, function, quality of life, or depressive 
symptoms. 
If ISOT is effective, we will additionally examine intervention impacts on VA healthcare 
utilization and costs. 
The overall significance of this project is to test the efficacy, satisfaction, and cost- 
effectiveness of a multifaceted patient-centered intervention designed to enhance opioid safety. 
Results will inform the utility of team-based care and risk mitigation strategies for chronic opioid 
therapy. If results are significant, clinicians will have a pragmatic evidence-based treatment that 
improves adherence to prescription opioids, reduces adverse events, does not result in 
impairments, and enhances the clinician-patient relationship. 
 
BACKGROUND 
The use of prescription opioid medications to treat chronic pain continues to increase.15 Data on 
opioid efficacy for chronic pain is limited.16 Studies indicate one-half of patients prescribed 
opioids will receive up to 30% improvement in short-term pain intensity.17,18 Less data are 
available on the long-term benefit of opioids.19,20 
Recent statistics are emerging about harms associated with the use of prescription opioids – 
this includes issues of misuse, abuse, diversion, and opioid-related adverse events. Prescription 
opioid misuse includes taking more medication than prescribed, requesting early refills, or using 
opioids for a non-prescribed purpose; this may occur in up to two-thirds of patients prescribed 
chronic opioid therapy (COT).8,9 More severe prescription opioid abuse, which includes 
concurrent illicit substance use and alcohol abuse, occurs in 10-35% of patients prescribed 
COT.10,21 Concurrent use of prescription opioids and alcohol or illicit substances increases 
likelihood of adverse effects, including overdose, and inhibits effective pain treatment.23 
Common adverse effects associated with opioid initiation include constipation, nausea, 
somnolence, and vomiting23 and up to 50% of patients discontinue opioids due to intolerance or 
insufficient pain relief.17,23 Patients maintained on COT may be exposed to additional adverse 
effects, including psychosocial and medical harms.24 Opioids are associated with increased 
likelihood of cardiovascular events, fractures, hypogonadism, sleep-related breathing disorders, 
and death.7 Unintentional overdose deaths quadrupled between 1999 and 2007, and in 16 U.S. 
states, more people die from prescription opioid overdose than automobile accidents.22 The 
dose of opioids is a major contributing factor in adverse effects3,4,6 and recent studies 
highlighted the relationship between opioid dose and elevated risk of overdose death.2,5 
Attempts to Reduce Harms from Prescription Opioids: Empirical studies and consensus 
guidelines support the routine use of urine drug tests (UDT) to assess adherence and screen for 
prescription opioid abuse among patients prescribed COT.25-27 The 2010 Department of 
Veterans Affairs Guidelines for treatment of patients prescribed COT recommend UDTs for all 
patients prior to initiating COT and periodic testing in stable patients. Increased frequency of 
UDT is recommended for patients at higher risk of abuse.11 These recommendations were 
highlighted by recent reports from the VA Office of Inspector General and Under Secretary for 

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Health.22 
Despite common calls for routine screening, regular UDT is uncommon. In prior research, we 
demonstrated that 26% of VA patients prescribed COT receive a UDT in a one year period.21 
Among patients at higher risk for prescription opioid abuse (such as patients with a substance 
use disorder), less than half receive UDT.21 These data replicate findings from other settings 
indicating UDTs are infrequently used.12,28-30 
Clinicians who routinely utilize UDT for all patients will encounter aberrant results in up to 40% 
of tests.31 Even among patients who appear compliant with COT, approximately 20% will test 
positive for an illicit drug or another non-prescribed opioid.26 These findings suggest clinicians 
over-estimate their ability to predict risk for prescription opioid abuse. Aberrant UDT results 
provide an opportunity to initiate discussions about treatment adherence and may lead to safe 
and appropriate changes in treatment. 
Reasons for limited use of UDT in standard practice include clinician discomfort with requesting 
UDTs, uncertainty about what to do with results, and lack of data on the effectiveness of UDT to 
reduce abuse.12,13 Beyond low rates of ordering UDTs, additional reasons for the limited utility of 
UDTs to reduce prescription opioid abuse include limited evidence-based treatments for 
patients who have aberrant UDT results.14 
There is a need for evidence-based treatments to reduce prescription opioid abuse in primary 
care. Prior research has tested methods to reduce prescription opioid abuse. These include 
intensive specialty care services,32,33 the Opioid Renewal Clinic (developed at Philadelphia 
VAMC34), and a multidisciplinary disease management program.35 These studies suggest that 
higher intensity multidisciplinary interventions designed to reduce prescription opioid abuse are 
acceptable to patients and may be effective. However, these programs are costly, labor-
intensive, and not feasible in busy primary care settings. Testing of pragmatic lower intensity 
interventions designed for primary care and other general practice settings is urgently needed. 
We propose a randomized trial of a multifaceted intervention to improve the safety of opioid 
prescribing. The intervention will have focus on enhancing the patient-clinician relationship, 
rather than placing patients and clinicians in conflict with one another about prescription opioid 
adherence monitoring. Primary care providers (PCPs) and PACT nurses will be randomized to 
the intervention, “Improving the Safety of Opioid Prescribing (ISOT)”, or the control group; 
patients will be nested within PCP status. All PCPs will participate in a workshop on reducing 
harms and misuse of prescription opioids, and maintaining open communication with patients. 
PCPs and PACT nurses assigned to ISOT will additionally collaborate with a nurse care 
manager (NCM) who will maintain a registry of enrolled patients, track UDT administrations and 
results, examine state Prescription Drug Monitoring Program (PDMP) data, monitor medical 
records, and collaborate with an internist and substance abuse specialist to provide decision 
support when patients demonstrate evidence of prescription opioid misuse or abuse. The NCM 
will also meet individually with enrolled participants to discuss strategies for reducing opioid 
adverse effects, preventing diversion, and providing rationale for prescription opioid adherence 
monitoring. Thus, the proposed treatment intervenes at the system, clinician, and patient levels. 
Our intervention is informed by the PRECEDE Model of health promotion,36 which posits that 
interventions likely to alter clinician behaviors and result in improved patient outcomes include 
three parts: predisposing (clinician education), enabling (feedback and monitoring), and 
reinforcing (system support) factors. Thus, the PRECEDE model indicates that system support 
and ongoing education or feedback is necessary to provide enabling and reinforcing 
components.37 
The intervention has a focus on shared-decision making and patient-centered care. Shared- 
decision making has been recommended to respect patient values and improve treatment 

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outcomes.38 Patient-centeredness includes taking a biopsychosocial approach, viewing the 
patient as an individual, sharing power and responsibility with the clinician, acknowledging the 
clinician is also a person, and maintaining a working alliance.39 Clinicians are often hesitant to 
use UDTs, for fear of damaging the patient-clinician relationship40 and screening for prescription 
opioid adherence may appear non-patient centered, particularly if framed as a mechanism to 
police activities or if screening is used to justify removal of prescription opioids. There are 
multiple mechanisms built into ISOT to support a patient-centered approach. Patient-centered 
interventions that have consistently resulted in improvements include the core components of 
effective communication, partnership, and health promotion.41 We will test whether 
randomization to ISOT, which includes collaboration with a NCM, will result in improvements in 
patient-centeredness. 
We propose a multifaceted intervention that operates at the patient, clinician, and system levels. 
Improving opioid safety necessitates collaboration and shared goals.43 Collaborative 
interventions have been effective in treating depression,44 severe mental illness,45 Alzheimer’s 
disease,46 diabetes,47 and other chronic diseases. We found that a collaborative care 
intervention improved pain, function, and depressive symptoms in veterans.48 Where prior 
research focused on a disease, the proposed study is unique in that we are addressing the 
safety of a treatment. We will increase evaluations for adverse effects and screenings for 
prescription opioid adherence, including follow-up with clinicians to ensure treatment changes 
are enacted. These interventions will be conducted in a way that enhances trust and rapport in 
the clinician-patient relationship. 
RESEARCH DESIGN AND METHODS 
Overview: We propose a randomized trial of multifaceted intervention designed to reduce 
adverse effects from prescription opioids, Improving the Safety of Opioid Prescriptions (ISOT). 
PCPs and PACT nurses will be randomized to ISOT or control; patients will be nested by 
clinician status. All clinicians will participate in an educational workshop on reducing harms of 
prescription opioid therapy and communicating with patients. Clinicians assigned to ISOT will 
collaborate with a nurse care manager (NCM) who will maintain a registry of enrolled patients, 
track UDT administrations and results, query prescription drug monitoring databases, monitor 
other evidence of potential problems, and collaborate with expert consultants to provide 
decision support when patients have evidence of prescription opioid misuse or abuse. The 
NCM will also have a one-time appointment with patients to discuss methods to reduce opioid 
adverse effects, prevent misuse and diversion, and provide rationale for prescription opioid 
adherence screening. The study will be conducted in the primary care clinics of the VA 
Portland Health Care System (VAPORHCS). A maximum of 80 PCPs, 80 PACT nurses, and 
350 patients will participate. We will randomize by clinician and patients will be nested within 
clinician intervention status. We will recruit participants who are currently prescribed chronic 
opioid therapy (defined below). Outcomes will be measured 6 and 12 months after enrollment. 
Summary of the Intervention: ISOT is based on the Chronic Illness Model,69 opioid treatment 
guidelines,11,16 and recent empirical research14,48. It is designed to optimize approaches for 
prescription opioid adherence monitoring, increase and support response when patients abuse 
opioids, and reduce adverse effects, all within a patient- centered context. Reducing harms from 
prescription opioids is complex and requires a multipronged approach.16,25,43 ISOT’s 
effectiveness will be enhanced by intervening with patients, clinicians, and the medical system. 
The chronic illness model enhances productive collaboration between patients and clinicians 
via education and activation, information systems changes, care management, and decision 
support.69 This model has been effective in treating diverse chronic health conditions.44-48 
Study Eligibility: Clinicians: All PCPs and their PACT nurse at the VAPORHCS will be eligible 
(~n=80 PCPs and ~n=80 nurses). In order for a PACT nurse to participate, the PCP s/he is 

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assigned to work with must also participate. Patients: All patients enrolled in primary care 
clinics will be potentially eligible. 
Inclusion Criteria: Participants must be enrolled in primary care at the VAPORHCS and be 
receiving chronic opioid therapy for chronic pain unrelated to a life-limiting disease. Chronic 
opioid therapy is defined as receiving an opioid prescription in three consecutive months or six 
prescriptions in the last year with a current prescription.20 The only other inclusion criterion is the 
ability to read and write in English. Exclusion Criteria: Age younger than 18 years, opioid 
therapy for palliative or end-of-life care, current enrollment in an opioid substitution program, or 
lack of telephone access. In order to identify potentially-eligible patients, we will request a 
waiver of informed consent, to access clinical diagnostic and pharmacy data. These data will be 
obtained for VA patients from the Corporate Data Warehouse, which will be accessed and 
analyzed within the secure user-restricted VINCI study site workspace. 
Recruitment: Clinicians: PCPs and PACT nurses will be recruited through email, discussions at 
practice meetings (the PI or a member of the research team will make a presentation during a 
weekly staff meeting and/or during the monthly meeting for all PCPs), and direct contact by the 
investigators. Patients: We will ask participating PCPs for permission to recruit patients. Using 
data from the electronic medical record, we will identify patients at the VAPORHCS who 
potentially meet our inclusion/exclusion criteria. Consistent with the VAPORHCS’s policy on 
recruiting participants for research studies, we will include a signed letter from the clinic 
manager where the patient receives his/her primary care, or director of primary care. The 
information sent to patients will include an accompanying letter from the research team, which 
will describe the study and invite patients to participate in a telephone screening session. 
Patients will be asked to return enclosed addressed and stamped letters, or to call our research 
office telephone number if interested in being screened. 
 
During the screening telephone call, the study research assistant will provide a brief overview of 
the study purpose (including clear statements that participation involves research and 
participation does not provide clinical treatment), review inclusion/exclusion criteria, describe 
what participation in the study would entail, and schedule participants for a study appointment 
session. If the patient is interested in participating and eligible, the patient will be scheduled for 
a baseline assessment (participants will also receive reminder phone calls and/or letters prior 
to their scheduled appointment). To enhance the likelihood of participants attending the study 
evaluation session, we will offer a $50 payment in compensation for time and/or travel to attend 
the research visit. The baseline assessment and all follow-up evaluations will be conducted 
either in person or via telephone. 
 
During the telephone screening visit, if the participant is ineligible, but is interested in receiving 
additional clinical services, s/he will be referred to the study PI to contact for information about 
relevant clinical resources. 
 
During the assessment sessions, the research assistant will provide another overview of study 
participation, administer informed consent and HIPAA authorization, and instruct the participant 
to complete study measures.  
 
Follow-up: In addition to the baseline assessment, all participants will complete follow-up 
assessments every 6 months. During these research visits, participants will complete all study 
measures (described below). Research visits must be completed within 1 month of the 
scheduled follow-ups (i.e., the 6-month follow-up must be completed between 5-7 months from 
baseline and the 12 month  follow-up must be completed between 11-13 months from 
baseline). 
 

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Randomization: We will randomize by PCP; patients will be assigned to intervention or control 
based on randomization of their PCP and PACT nurse. At the VAPORHCS, each PACT nurse is 
assigned to a single PCP and each PCP has one PACT nurse. Thus, contamination effects by 
PACT nurses working with a PCP assigned to the control condition will not be a concern. 
We will randomize dyads of PCPs and PACT nurses to receive ISOT versus control. We will 
initially recruit PCPs to participate. Of the PCPs who choose to enroll, we will then attempt to 
recruit the PACT nurse who is assigned with that PCP (if the PACT nurse chooses not to 
participate, the consenting PCP will remain eligible). We will randomly assign consenting PCPs 
and their PACT nurse (if they choose to participate) to receive ISOT versus control after 
stratifying by PCP type of training (physician versus nurse practitioner/physician assistant) and 
proportion of patients in PCP’s panel receiving opioid prescriptions (high vs low, based on 
median eligible clinician population). Using a permuted block randomization and maximizing the 
number of PCPs (clusters) should prevent major uneven distribution of caseload size and 
unmeasured confounders across the arms and increase precision.72 The statistician will perform 
the randomization prior to patient recruitment. Research assistants will be blind to intervention 
status of patients and clinicians. 
ISOT Team and Training: ISOT is a multifaceted intervention. The individual components of 
ISOT have been adapted from recommendations made as part of clinical practice 
guidelines11,16, our preliminary studies, and prior experience in conducting multifaceted 
intervention studies in primary care.48,51 The unique aspect of this intervention is its integration 
of different treatment recommendations into a single package. This method of treatment 
development and adaptation is similar to other effective multifaceted interventions.73 
A research nurse care manager (NCM) will be hired to participate in this study. The NCM will be 
employed full-time. S/he will have clinical training as a registered nurse (R.N.) and have a 
clinical background in chronic disease management and outpatient treatment. The individual will 
receive extensive training in chronic pain management, prescription opioid safety, and research 
procedures. 
ISOT Intervention Components  
Clinician Education and Activation: 
a. Dr. Erin Krebs (Co-Investigator) who has expertise in chronic pain treatment, screening for 
prescription opioid adherence, and patient-centered care, will conduct the interactive 
workshop for PCPs and PACT nurses. She has experience providing trainings on this topic. 
Workshop goals will be designed to optimize clinician interactions via education and 
activation, while providing system support. We will educate clinicians about prescription 
opioid adherence monitoring, screening for adverse effects, and enhancing patient-centered 
care, with a focus on enhancing shared decision making.74,75 Clinical recommendations for 
modifying care in instances of opioid misuse/abuse will be based on our pilot work.14 The 
workshop will include brief lecture, demonstration, and participatory skill development. The 
workshop will be conducted one time, during normal business hours, at the VA Portland 
Health Care System main campus. The session will be video-recorded. Clinicians who wish 
to participate in the study, but are not able to attend the in-person session, may view the 
recording. 
b. The NCM will meet with each PCP and PACT nurse to discuss the intervention, identify 
attitudes about screening for prescription opioid adherence, and best practices for 
communicating about these topics. 
c. The primary care pain specialist (Dr. Carr) and substance abuse specialist (Dr. Lovejoy) will 
provide consultation to Dr. Krebs in the development of the training workshop and help 
facilitate role-play components of the training. They will also consult with the NCM throughout 

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the intervention as needed. 
d. The NCM will monitor medical records for all enrolled participants to examine potential opioid 
non-adherence behaviors (e.g., lost prescriptions, early refills, multiple providers, seeking 
opioids from the ER). For patients with prescription opioid abuse, the NCM will offer decision 
support to PCPs about treatment options. The NCM will also follow-up to ensure successful 
implementation of changes in the treatment plan. 
e. Because there is variation in how clinicians approach preventing harms from opioids, ISOT 
will take a flexible approach to accommodate clinician preferences; we will collect information 
about PACT team preferences for communication with the intervention team and opinions on 
opioid prescribing and monitoring prior to initiating the study.53 The NCM will provide 
feedback to each PACT team member in a manner consistent with preferences (i.e., 
discussion, chart note, secure email, voicemail message, etc.). 
f. An email list serve (using encryption and within security firewall), managed by the PCP pain 
specialist and substance abuse psychologist, will be used to communicate information and 
facilitate dialogue among intervention clinicians. In our prior research, clinicians appreciated 
the opportunity to discuss (non-identifying) patient management strategies. 
Patient Assessment and Activation: 
a. The NCM will perform a chart review of each ISOT patient and schedule a one-time 
appointment. 
b. With each patient, the NCM will review strategies for increasing opioid safety and discuss 
adverse effects of prescription opioids, including overdose, falls, interactions with 
other medications or alcohol/illicit substances, and opioid withdrawal. The NCM 
and patient will discuss treatment goals, options, and self-management 
approaches. Supplemental written materials will describe risks and benefits of 
opioids. With PCP authorization, the NCM will facilitate completion of opioid 
treatment agreements, as needed. 
c. The NCM will talk with patients about the rationale behind screening for adherence to 
prescription opioids with UDTs, and focus on patient safety and risk-benefit of opioids.76 
d. Patients will receive information about common ways in which opioids are diverted (e.g., 
taken by friends or family, loaning to others) and potential harms associated with diversion. 
Recommendations will be provided about methods for limiting opioid diversion, including 
details about medication take-back programs. 
e. Patients will be provided with an easy-to-read brochure that describes adverse effects of 
opioids, methods for reducing the likelihood of adverse events, rationale for prescription 
opioid abuse screening, and strategies for preventing diversion. 
System Assessment and Intervention: 
a. The NCM will maintain a registry of all enrolled patients and track UDT administrations. For 
patients that have not been administered a UDT in 12 months or more, the NCM may 
assist  the PCP by placing an order for UDT in the medical record. 
b. The NCM may write the UDT order for the PCP to sign, if requested. The NCM will 
recommend checking state PDMP data for patients without a past-year check. (In Oregon, 
only prescribers are authorized to check PDMP data, so the NCM could not query the 
database him/herself.) PDMP checks are recorded in the medical record, so the research 
team will be able to track frequencies. If requested, the NCM may collaborate with pharmacy 
services to query state PDMP databases. 
c. The NCM will provide support about methods for talking with patients about prescription 
opioid adherence monitoring. This will include providing written information, such as scripts 
that clinicians can follow. In pilot interviews, PCPs requested these types of materials to 
assist with improving adherence to opioid regimens. 
d. The NCM will track all UDTs, notify the PCP of the results, and provide recommendations 

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04/24/2019
about potential next steps in care. With collaboration from the primary care pain specialist 
and substance abuse specialist, the NCM will provide decision support to clinicians for 
patients with prescription opioid abuse about options for modifying treatment. The NCM will 
also follow-up to ensure treatment recommendations are implemented. 
e. With input from the NCM, some patients may be referred for supplemental treatment. This 
may include referral to the substance abuse treatment program, mental health clinic, or 
specialty pain service. 
f. The NCM may identify potential side effects or risky prescribing practices that have not been 
addressed. 
Follow-up: Participants will complete assessments at baseline, 6 months, and 12 months post- 
baseline. During research visits, participants will complete all study measures, the structured 
clinical interview, and provide a UDT. 
In the course of the study, a PCP or PACT nurse who is enrolled in the study may leave their 
position at the Portland VA. Clinical patients who are “attached” with these clinicians and 
already enrolled in the study will remain eligible to participate. Clinical recommendations that 
are made regarding patient’s care will continue to be forwarded to PACT team members who 
remain involved in each patient’s treatment. 
 
Participants will also be reimbursed for their participation. This includes a $50 payment for each 
of the three research visits. This reimbursement amount is intended to cover time to complete 
the research visit and/or costs of travel to and from the hospital for the visit. This payment 
amount is consistent with other research studies of similar duration and intensity and do not 
appear to put undue pressure on prospective subjects to participate. The payment amount and 
terms of payment are specified in the consent form. 
Study Measures: Research staff collecting outcome data will be blinded to participant and 
clinician intervention status. The ISOT intervention team will not have access to data obtained 
by the research team, except if a patient’s safety becomes a concern. 
 
Measures of Prescription Opioid Misuse/Abuse (Aim 1): The primary outcome will be responses 
to the Current Opioid Misuse Measure (COMM), a 17-item self-report measure that assess 
several constructs that place individuals at risk of opioid misuse and abuse.80 The COMM has 
been well-validated81 and previously used in treatment outcome studies. 33 The COMM has 
also been validated for detecting prescription opioid abuse using cut-off scores > 13.82 The 
Timeline Followback (TLFB; Sobell & Sobell, 1992) is a frequently used assessment method 
that uses calendar prompts to elicit frequency and intensity of substance use within a specific 
timeframe. The TLFB has demonstrated good test-retest reliability and validity for assessing 
multiple substances at one time (Ehrman & Robbins, 1994; Fals-Stewart et al., 2000). This 
study will assess use of alcohol, marijuana, methamphetamine, cocaine, heroin, opioid 
medications, and other prescription medications in the past 30 days. The Structured Clinical 
Interview for DSM-IV (SCID) will be used to assess current alcohol and substance use 
disorders.83 The Overdose Risk Behavior (ORB) questionnaire is a newly-developed 14-item 
self-report measure that assesses behaviors and activities that place an individual at higher 
risk of accidental overdose to prescription opioids. Participants will complete Urine Drug 
Tests, to evaluate for the presence of substances ingested by participants. UDTs will 
evaluate for cannabis, cocaine, amphetamines, benzodiazepines, barbiturates, opioids, and 
opiates. Confirmatory testing will be conducted for all UDT screens with unexpected results. 
The purpose of UDT is to have confirmation of certain prescribed and illicit substances that 
have been ingested by study participants. Research staff will review medical record data 
prior to research visits to confirm medication status. Participants will be asked to validate 
pharmacy data about prescription status, as well as medications prescribed outside VA. 

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Measures of Opioid-Related Adverse Events (Aim 1): Prior to appointments, research staff will 
review medical record data for participants to track adverse effects. This includes emergency 
room visits, falls, and overdoses. Participants will also be queried about these effects and motor 
vehicle accidents at each research visit. 
 
Processes of Care Measures (Aim 2): At the conclusion of each patient’s participation, we will 
extract medical record data on treatment received. This will include data in the year prior to 
treatment and the year during treatment on number of urine drug tests administered as part of 
clinical care and queries to state prescription drug monitoring programs. Results from UDTs 
administered as part of clinical will be extracted from VA laboratory data. For PDMP data, 
clinicians must provide documentation in the medical record when they check PDMP data; 
research staff will review medical record data of enrolled participants to evaluate incidence and 
frequency of PDMP checks. For patients with documented prescription opioid abuse, research 
staff will evaluate any potential changes in care that occurred following detection of prescription 
opioid abuse, including referrals to the substance abuse treatment program, mental health 
clinic, or specialty pain service; follow-up monitoring; and treatment changes (such as change in 
opioid or dose, cessation of opioids, etc.). 
➢ Although the research team will monitor UDTs administered in clinical practice, these data 
will not be used as outcome to reflect prescription opioid abuse. UDTs will also be 
administered by the research team to all participants at all three research visits. These UDTs 
will be ordered under a unique study identification number. Patient identifying information will 
never be attached to or associated with these research UDT sample. 
Measures of the Clinician-Patient Relationship (Aim 2): Trust in one’s clinician will be examined 
with the Trust in Physician Scale, an 11-item self-report measure assessing interpersonal trust 
in patient-clinician relationships.84 This has been used extensively as an outcome measure in 
prior studies examining patient-centered care and shared decision-making, and has evidenced 
strong psychometric properties.85 The Participatory Decision Making Style is a brief self-report 
measure designed to assess level of patient involvement in medical decision making.86 Both of 
these measures have been shown to be responsive to interventions directed towards by PCPs 
and allied health professionals.87 The Positive Regard Scale is a recently-developed 5-item self- 
report measure that evaluates patients’ perceptions of their relationship with their primary care 
provider. 
Clinician Data (Aim 2): At baseline and 12 months, PCPs and PACT nurses will be queried 
about barriers to screening for prescription opioid abuse. Clinicians and PACT nurses will also 
be asked about their satisfaction with and ways to improve ISOT, how ISOT affected their 
relationships with patients, and their encounters with patients prescribed opioids (see Appendix 
II for the questionnaire, which is based on our prior research48). 
Measures of Pain, Function, and Quality of Life (Aim 3): The Chronic Pain Grade (CPG) will be used 
to assess pain intensity and function. The CPG is a commonly used and well-validated measure that 
provides global scores of pain intensity and function.88,89 Quality of life will be measured with the 
Veterans RAND 12-Item Health Survey (VR-12),90 a 12-item well-validated self-report measure of 
quality of life that is sensitive to changes in veterans and has been used in numerous epidemiological 
studies. The Patient Health Questionnaire (PHQ), a brief and psychometrically valid measure, will be 
used to assess depressive symptoms.91,92 The Generalized Anxiety Disorder-7 (GAD-7) Scale is a 
self-report measure designed to assess the severity of anxiety symptoms, and has been validated as 
a robust predictor of the different anxiety disorders (Spitzer, Kroenke, & Williams, 1992; Kroenke, 
Spitzer, Williams, Monahan, & Lowe, 2007). Side effects from opioids will be assessed with the side 
effects subscale of the Pain Treatment Satisfaction Scale.93 
Basic demographic characteristics that will be assessed include age, gender, race, ethnicity, 
marital status, employment, socioeconomic status, disability status, and VA service-connection. 

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Medical utilization data will be obtained from the medical record (e.g., physical therapy, surgery, 
osteopathic manipulation, acupuncture, etc.) and self-report (non-pharmacological interventions, 
complementary and alternative medicine approaches to pain140). Text data obtained from notes 
in the clinical record may also be reviewed, in order to conduct analyses of natural language 
processing. Based on pilot testing of this battery, we anticipate that all of the above 
questionnaires will be completed by participants in less than 60 minutes. 
Data Analysis 
Before analyses are conducted, data will be audited for quality, including missing data patterns 
and evaluation of distributions with reference to planned models. The evaluation of distributions 
includes the detection of outliers and checking distributions of variables to ensure they meet the 
assumptions of planned analyses. Power calculations (below) are based on comparisons that 
account for data clustering. The analyses will incorporate covariate information, in particular 
baseline data on age, gender, depressive symptoms, history of substance use disorder (SUD), 
and pain intensity, which will enhance power. Analyses will be conducted with all enrolled 
participants regardless of attrition (intent-to-treat). 
Management of Missing Data: The pattern of missingness will be examined and baseline 
responses compared between those with and without missing data. Variables related to 
missingness will be included in the analyses which should yield valid inferences.94 We will 
complete sensitivity analyses comparing the main analyses results with results from analyses 
with completers only and based on multiple imputation. 
Aim 1: Evaluate to what extent a multifaceted intervention (ISOT) enhances prescription opioid 
safety. For Aim 1, we use 8.9 as an estimated standard deviation (SD) for change in COMM 
score from baseline to 12 months.95 This SD estimate is based on SD’s in prior intervention 
research.33 For each group, an estimated SD for change was obtained using the two SD’s and a 
within-subject intraclass correlation coefficient of 0.5. Averaging the corresponding variances 
leads to an estimated variance of 78 (SD=8.9). Power calculations were performed based on 28 
or 32 (we may recruit as many as 40 PCPs, but do not anticipate that many will consent to 
participate) clinicians per intervention group and 4 or 5 patients per clinician. Table 1 displays 
differences in mean COMM change that can be detected under various values for the intraclass 
correlation coefficient. Our targeted sample size is 32 PCPs per group and an average of 5 
patients per PCP. With a loss of 4 PCPs per group and 1 patient per PCP, we have power to 
detect small differences (able to detect < ½ SD change) in COMM score. For Aim 1, we plan to 
conduct analyses for self-report data (COMM), clinical interviews to assess current substance 
use disorders (SCID), and results from research-administered UDTs. With n=320, 28-32 PCPs 
per group, and 4-5 patients per PCP, we have statistical power greater than 80% to detect 
differences in percentages between groups of about 20% for binary outcomes from the SCID 
and UDT (see Table 2 below for similar calculations for Aim 2). 
Table 1. Mean differences in COMM change from baseline to 12 months (80% power, =0.05). 
Intra-class 
correlation 
Mean detectable difference 
in COMM change, n=4 
patients per clinician, 28 
clinicians per intervention 
Mean detectable difference 
in COMM change, n=4 
patients per clinician , 32 
clinicians per intervention 
Mean detectable difference 
in COMM change, n=5 
patients per clinician, 28 
clinicians per intervention 
Mean detectable 
difference in COMM 
change, n=5 patients 
per clinician, 32 
clinicians per 
intervention 
0 
3.4 
3.2 
3.0 
2.8 
0.02 
3.5 
3.3 
3.2 
2.9 
0.04 
3.6 
3.4 
3.3 
3.1 
0.10 
3.9 
3.6 
3.6 
3.4 
Analyses for Aim 1: For comparison of mean COMM change between groups, we will use mixed 
longitudinal models with a random effect for PCP and patient, to allow for correlation between 

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observations on different patients of the same PCP and for repeat observations on the same 
patient. Transformations of the outcome will be applied to meet distributional assumptions. In 
linear mixed models, we will include baseline measures of patient age, sex, depressive 
symptoms, history of SUD, and pain severity as covariates. We will also adjust for baseline 
COMM score and the stratification variables: PCP training type (MD/DO vs NP or PA) and 
proportion of clinician panel prescribed opioids (high vs low). In addition to testing formal 
hypotheses, we will report 95% confidence intervals for the differences in mean outcomes 
between groups at 6 and 12 months. For comparing the percentages classified as abusing 
opioids at any time during follow-up (combining information from 6 and 12 months) based on 
SCID and UDT, we will use generalized-linear mixed models (or GEE as needed) to incorporate 
correlation between patients of the same PCP. We will adjust for baseline measures of the 
outcome along with the covariates listed above. We will report group comparisons on all three 
primary outcomes (COMM, SCID, and UDT). Assuming 15% of the sample has current SUD 
(consistent with other research), we will not conduct interaction analyses, but will present 
descriptive information for each outcome (COMM, SCID, UDT) on treatment outcomes by SUD 
status. 
Data on opioid-related adverse events will be obtained from the medical record and participant 
self-report. We will collect data on opioid overdoses, falls, motor vehicle accidents, and ER visits 
related to pain. Descriptive information on opioid-related events will be provided for each group 
(numbers and percentages of participants with each event). Because the number of adverse 
events is expected to be small, we will use Fisher’s Exact test to compare adverse event 
percentages between groups. We will compare both percentages for each individual type of 
event as well as percentages of individuals experiencing any adverse event between groups. 
Aim 2: Assess to what extent assignment to ISOT impacts pain treatment-related processes of 
care. The power for comparing the percentage of patients receiving at least one UDT (Table 2) 
during the follow-up period assumes that 40% of patients in the control group will receive a 
follow-up UDT.21 This is based on prior research in which 26% of patients had a UDT and our 
expectation that the percentage will be higher in the control group after clinicians participate in 
the workshop. We hypothesize that clinicians randomized to ISOT will have much higher rates 
of administering UDTs. Regarding checks to state PDMPs, currently 3-5% of VAPORHCS 
patients receive a query. The ISOT protocol prompts the clinician to conduct PDMP queries and 
we expect dramatic differences in percentages between the two intervention groups. We have 
80% power to detect differences of 17-21% on binary outcomes. Here we expect differences to 
be much greater. 
Table 2. Urine drug test percentage in the intervention group that can be detected (80% power,  
=0.05) compared to a usual care percentage of 40%. 
Intra-class 
correlation 
Urine drug test % that can be 
detected in the intervention 
group, n=4 patients per clinician, 
28 clinicians per intervention 
Urine drug test % that can 
be detected in the 
intervention group, n=4 
patients per clinician, 32 
clinicians per intervention 
Urine drug test % that can 
be detected in the 
intervention group, n=5 
patients per clinician, 28 
clinicians per intervention 
Urine drug test % test 
that can be detected 
in the intervention 
group, n=5 patients 
per clinician, 32 
clinicians per 
intervention 
0 
59% 
57% 
57% 
56% 
0.02 
59% 
58% 
57% 
56% 
0.04 
60% 
58% 
58% 
57% 
0.10 
61% 
60% 
60% 
58% 
 
Aim 2: Assess to what extent assignment to ISOT impacts the quality of the clinician-patient 
relationship. Using estimated standard deviations of 14.2 for the Trust in Physician Scale84 and 

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28.9 for the Participatory Decision Making Style,86 we have 80% power at level 0.05 to detect 
mean differences between groups at 12 months of 4.7 to 5.6 and 9.6 to 11.3, respectively, using 
28 to 32 clinicians per intervention group, 4 to 5 patients per clinician, and assuming an 
intraclass correlation of 0.02. If we focus on change from baseline, the power for comparisons at 
each follow-up time-point would be greater, provided the correlations between baseline and 
follow-up are greater than 0.5. 
Clinicians will also be queried about perceived barriers and potential effectiveness of screening 
for prescription opioid abuse. With 32 pairs of clinicians and intra-class correlations of 0 to 0.1, 
we would have 80% power to detect differences of 0.50 to 0.53 SDs in mean summary scores 
between intervention groups. 
Analyses for Aim 2: We will use GEE methods to compare between intervention groups the 
average numbers of clinical care documentations, UDT’s, and queries to state prescription drug 
monitoring programs while adjusting for the baseline covariates (age, gender, depressive 
symptoms, history of SUD, and pain intensity) and the stratification variables. For patient level 
data, the approach for analysis of Aim 2 outcomes will be the same as that for Aim 1, using 
mixed models and/or GEE for comparisons, while adjusting for baseline covariates (age, sex, 
depressive symptoms, history of SUD, and pain severity), baseline measure of the outcome, 
and stratification variables (training type, proportion of panel prescribed opioids). Analyses will 
be conducted for scores on the Trust in Physician scale and Participatory Decision Making 
Style. 
For clinician and PACT nurse data on barriers to use of UDTs, we will report descriptive 
information for each of the 10 questions. For each group and question, we will report the 
percentage of clinicians with scores at each of the five levels at baseline and follow-up, as well 
as the corresponding descriptive change information in follow-up scores. We will report the 
mean (and SD, min, max) of change for each question. We will obtain an overall survey score, 
by averaging the 10 question scores for each clinician and report the mean (and SD, min, max) 
survey score for each group at baseline and follow-up, along with the mean change (and SD, 
min, max) in survey score. We will use linear mixed models on the change scores to compare 
groups, while adjusting for clinician age, clinician training type, sex, panel size, summary survey 
score at baseline, and stratification variables. We will include a random pair effect to allow for 
correlation between the PCP and PACT nurse within each pair. P-values and 95% confidence 
intervals for the differences between groups in change scores will be provided. Similar analyses 
will be conducted for examining survey data on clinician satisfaction with ISOT. 
Aim 3: Explore to what extent ISOT is associated with changes in pain intensity, function, quality 
of life, and depressive symptoms. Our goal is to improve opioid safety while not adversely 
impacting pain or quality of life. Power calculations were performed for pain intensity based on 
28 or 32 clinicians per intervention group and 4-5 patients per clinician, using data indicating a 
baseline mean pain intensity score of 66 and estimated SD of change from baseline of 13.1.48 
Here we are interested in tests of non-inferiority; specifically we aim to show that the reduction 
in mean pain intensity for the ISOT group is no more than one-half standard deviation less than 
that for the control group. Considering the same set of conditions given in Tables 1-2, the worst 
case scenario (n=4 patients per clinician, n=28 clinicians per intervention and intraclass 
correlation of 0.10), results in power greater than 80% at level 0.025 for testing non-inferiority of 
pain intensity if the true mean change for ISOT patients is not more than 0.9 units less than the 
control group. Similarly for change in pain-related function, quality of life, and depressive 
symptoms, we considered a one-half SD difference in change to be the appropriate non- 
inferiority limit.96,97 We have power greater than 80% at level 0.025 for testing non-inferiority, if 
the mean change in function, quality of life, and depressive symptoms for the ISOT group are no 
more than 1.8, 0.9, and 0.4 units, respectively, less than those for the control group. These are 
based on estimated SD’s for change assuming a 0.5 correlation between baseline and follow-

14 
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up. 
Analyses for Aim 3: For analysis of pain intensity, we hypothesize that the mean reduction in 
pain (measured by CPG pain intensity scale) for patients randomized to ISOT is no more than 
6.6 points less than the control. Although we will conduct a formal test of this non-inferiority 
hypothesis, our primary goal is to estimate the difference in mean reductions between the 
groups at each time-point, anticipating that the differences will be small. Construction of a lower 
bound (97.5%) corresponds specifically to the non-inferiority hypothesis of interest, but we will 
also obtain the corresponding (97.5%) upper bound. As with comparison of continuous 
outcomes for the other aims, mixed models will be used for hypothesis testing and confidence 
interval construction and we will adjust for covariates measured at baseline (patient age, sex, 
depressive symptoms, history of SUD, and pain severity), stratification variables, and baseline 
score. We will use the same approach to examine potential differences between groups on 
measures of pain-related function, quality of life, and depressive symptoms. 

15 
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