white fifer thesis final

Impact Evaluation of A Prescription Drug Monitoring Program (PDMP) Use Policy Based on

Guidelines and Best Practices

Matthew White

Micaiah Fifer

A thesis submitted in partial fulfillment of the requirements of the

Masters of Healthcare Administration Program

Pacific University

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Abstract

Introduction From the outset of the implementation of pain management drugs, aberrant behavior relating to controlled substances such as opioids has led to public safety concern, abuse, addiction, and increased risk of overdose and mortality. Beyond the prescribing physicians, some pharmacies have implemented a Prescription Drug Monitoring Program (PDMP) use policy and started reviewing patient records at the point of prescription filling, which rely on data from Oregon’s PDMP database. There are several sets of guidelines for prescribers to use when prescribing these medications that are supported by research, and can be used to develop PDMP use policies by pharmacies and regulatory bodies. Methodology This study has utilized the Oregon PDMP by applying a set of criteria to a de-identified sample of opioid-prescribed patients in an effort to evaluate effectiveness in reducing the following aberrant behaviors. These criteria include: (1) overutilization by maximum morphine equivalent daily dose or maximum methadone daily dose, (2) multiple long-acting or multiple short-acting concurrent opioid prescriptions, (3) multiple concurrent prescribers, (4) multiple concurrent pharmacies, (5) concurrent prescribing of an opioid and benzodiazepine or opioid, benzodiazepine and carisoprodol, and (6) early refills from those filling opioid prescriptions at a retail pharmacy. Results Of these criteria, we found that implementation of the PDMP use policy resulted in a statistically significant decrease in the proportion of patients with a maximum daily morphine equivalent dose >120 mg, maximum daily methadone dose >40 mg, concurrent benzodiazepines, opioids, and carisoprodol, filling of prescriptions from multiple concurrent providers, and filling of prescriptions at multiple concurrent pharmacies. Recommendations Based on the results of this study and due to the risks of aberrant behaviors as summarized in the literature in Chapter 2, which indicate a public safety concern, abuse, addiction, or increased risk of overdose and mortality, we recommend requiring pharmacy PDMP use policies for all pharmacies that include the following criteria: (1) maximum daily morphine equivalent dose >120 mg, (2) maximum daily methadone dose >40 mg, (3) concurrent benzodiazepines, (4) opioids, and carisoprodol, (5) filling of prescriptions from multiple concurrent providers, and (6) filling of prescriptions at multiple concurrent pharmacies.

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Table of Contents

CHAPTER 1 - INTRODUCTION ...................................................................................................... 1

Introduction ......................................................................................................................................... 1

Purpose Statement ............................................................................................................................. 2

Research Question .............................................................................................................................. 3

Thesis .................................................................................................................................................... 3

Rationale for Thesis ........................................................................................................................... 4

Rationale for Project ......................................................................................................................... 4

Summary .............................................................................................................................................. 5

CHAPTER 2 – LITERATURE REVIEW ........................................................................................ 6

Opioid Abuse ....................................................................................................................................... 6

Prescribing guidelines for chronic pain ......................................................................................... 8

Prescription Drug Monitoring Program ....................................................................................... 9

Prescribing Guidelines – Dosing Limits ...................................................................................... 12

Screening Criteria ............................................................................................................................ 14

Barriers In Implementing Screening Tools ................................................................................ 15

CHAPTER 3 - METHODOLOGY................................................................................................... 18

Introduction ....................................................................................................................................... 18

Research Methodology .................................................................................................................... 18

Research Methods ............................................................................................................................ 19

Sample Eligibility Criteria ........................................................................................................................... 19

Sample Exclusion Criteria ........................................................................................................................... 19

Data Collection ................................................................................................................................................ 20

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Data Analysis ................................................................................................................................................... 23

Data Security and Handling ........................................................................................................... 25

Reporting Findings .......................................................................................................................... 25

Ethical Considerations .................................................................................................................... 25

Summary ............................................................................................................................................ 27

CHAPTER 4 - RESULTS ................................................................................................................... 29

Maximum Morphine Equivalent Daily Dose ............................................................................. 29

Concurrent Benzodiazepines and Opioids .................................................................................. 39

Concurrent Benzodiazepines, Opioids, and Carisoprodol ...................................................... 44

Multiple Concurrent Prescribers .................................................................................................. 49

Multiple Concurrent Pharmacies ................................................................................................. 54

CHAPTER 5 - CONCLUSIONS ....................................................................................................... 59

Summary of Findings ...................................................................................................................... 59

Maximum Morphine Daily Dose ............................................................................................................... 59

Maximum Methadone Daily Dose ............................................................................................................ 61

Concurrent Benzodiazepines and Opioids .............................................................................................. 63

Concurrent Benzodiazepines, Opioids & Carisoprodol ..................................................................... 64

Multiple Concurrent Prescribers ................................................................................................................ 65

Multiple Concurrent Pharmacies ............................................................................................................... 66

Actual Patient Case Examples ....................................................................................................... 67

Recommendations for the sponsoring organization ................................................................. 68

Recommendations for Regulatory Bodies ................................................................................... 68

General approaches for future research ..................................................................................... 69

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Limitations of the study .................................................................................................................. 69

Conclusions ........................................................................................................................................ 71

References .............................................................................................................................................. 72

APPENDIX A - Sponsor Support Letter ...................................................................................... 80

APPENDIX B - Multnomah County Health Department IRQ ............................................... 81

APPENDIX C - Principal Investigator Certification De-identified PHI ................................. 98

APPENDIX D - Public Health IRB Approval Letter ................................................................... 99

APPENDIX E - Southern Oregon IRB Approval Letter ......................................................... 100

APPENDIX F - Pacific University IRB Exempt Application .................................................. 101

APPENDIX G - Pacific University IRB De-identified HIPAA Form .................................... 107

APPENDIX H - Pacific University IRB Approval Letter ........................................................ 110

APPENDIX I - Timeline .................................................................................................................. 111

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Tables and Figures

Table 1: Max Morphine Equivalent Daily Dose (mg) at the Intervention Pharmacy ........................30

Table 2: Max Morphine Equivalent Daily Dose (mg) at the Control Pharmacy ...............................31

Table 3: Results Summary – Maximum Morphine Equivalent Daily Dose (Intervention) ...............32

Figure 1: % Patients >120 mg Morphine Equivalent Daily Dose (Intervention) ..............................32

Table 4: Results Summary – Maximum Morphine Equivalent Daily Dose (Control) ......................33

Figure 2: % Patients >120 mg Morphine Equivalent Daily Dose (Control) .....................................33

Table 5: Maximum Daily Methadone Dose (mg) at the Intervention Pharmacy ..............................35

Table 6: Maximum Daily Methadone Dose (mg) at the Control Pharmacy ......................................36

Table 7: Results Summary – Maximum Methadone Daily Dose (Intervention) ...............................37

Figure 3: % Patients >40 mg Methadone Daily Dose (Intervention) ................................................37

Table 8: Results Summary – Maximum Methadone Daily Dose (Control) ......................................38

Figure 4: % Patients >40 mg Methadone Daily Dose (Control) ........................................................38

Table 9: Concurrent Benzodiazepines and Opioids at the Intervention Pharmacy ...........................40

Table 10: Concurrent Benzodiazepines and Opioids at the Control Pharmacy .................................41

Table 11: Results Summary – Concurrent Benzodiazepines and Opioids (Intervention) .................42

Figure 5: % Patients on Concurrent Benzodiazepines and Opioids (Intervention) ...........................42

Table 12: Results Summary – Concurrent Benzodiazepines and Opioids (Control) .........................43

Figure 6: % Patients on Concurrent Benzodiazepines and Opioids (Control) ...................................43

Table 13: Concurrent Benzos, Opioids, and Carisoprodol at the Intervention Pharmacy .................45

Table 14: Concurrent Benzos, Opioids, and Carisoprodol at the Control Pharmacy ........................46

Table 15: Results Summary – Concurrent Benzos, Opioids, and Carisoprodol (Intervention) .........47

Figure 7: % Patients on Concurrent Benzos, Opioids, and Carisoprodol (Intervention) ...................47

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Table 16: Results Summary – Concurrent Benzos, Opioids, and Carisoprodol (Control) ................48

Figure 8: % Patients on Concurrent Benzos, Opioids, and Carisoprodol (Control) ..........................48

Table 17: Multiple Concurrent Prescribers at the Intervention Group ..............................................50

Table 18: Multiple Concurrent Prescribers at the Control Group ......................................................51

Table 19: Results Summary - Multiple Concurrent Prescribers (Intervention) .................................52

Figure 9: % Patients Filling Prescriptions from Multiple Concurrent Prescribers (Intervention) .....52

Figure 10: Breakdown of Multiple Concurrent Prescribers (Occurrences by Individual) .................52

Table 20: Results Summary - Multiple Concurrent Prescribers (Control) ........................................53

Figure 11: % Patients Filling Prescriptions from Multiple Concurrent Prescribers (Control) ..........53

Table 21: Multiple Concurrent Pharmacy Filling at the Intervention Group ....................................55

Table 22: Multiple Concurrent Pharmacy Filling at the Control Group ............................................56

Table 23: Results Summary – Multiple Concurrent Pharmacies (Intervention) ................................57

Figure 12: % Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Intervention) ..57

Figure 13: Breakdown of Multiple Concurrent Pharmacies (Occurrences by Individual) ................57

Table 24: Results Summary – Multiple Concurrent Pharmacies (Control) .......................................58

Figure 14: % Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Control) ..........58

IMPACT EVALUATION OF A PDMP USE POLICY

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CHAPTER 1 - INTRODUCTION

Introduction

Since the introduction of pain management drugs, aberrant behavior relating to misuse of

controlled substances such as opioids has led to increased dependence which ultimately can lead

to deaths due to overdose. This development is reflected in the fact that “unintentional

prescription opioid overdose death rates (not including methadone) have increased by 5.5 times

from 0.5/100,000 [people in 2000] to 2.5/100,000 [people in 2011]” (Oregon Center for Health

Statistics, 2013) where the denominator represents the population of individuals utilizing opioids

from years 2000 to 2011 per 100,000 Oregonians. The increase in overdoses highlights a need

for processes that address the issue of opioid aberrant behavior. One potential solution lies in the

implementation of more stringent PDMP use policies directed towards those utilizing opioids as

a form of pain management.

The Oregon PDMP is essentially a tool meant to give providers and pharmacists the

ability to review statewide patient prescription history and assist them in managing prescribing

practice. The PDMP utilizes “information provided by Oregon-licensed retail pharmacies”

(Oregon PDMP Public Portal, 2014). Pharmacies submit prescription data to the PDMP system

for prescriptions that have been dispensed to Oregon residents. This information is protected

health information and is collected and stored securely.

“Oregon-licensed healthcare providers and pharmacists and their staff may be authorized for an account to access information from the PDMP system. Bordering state licensed healthcare providers may also be authorized for access accounts. By law their access is limited to patients under their care” (Oregon PDMP Public Portal, 2014). The purpose of creating the PDMP was to support the appropriate use of

prescription drugs. “The information is intended to help people work with their healthcare

providers and pharmacists to determine what medications are best for them” (Oregon PDMP


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Public Portal, 2014). Research has been done by many groups, and there are several sets of

guidelines (payers, prescribing groups, and professional associations) for prescribers to use when

prescribing these medications. Guideline specifics are covered in the literature review section of

this study (chapter 2). Beyond the prescribing physicians, some pharmacies in the state have

begun implementing PDMP use policies at the point of prescription filling, which rely on data

from Oregon’s Prescription Drug Monitoring Program (PDMP) Database. The utilization of

pharmacists as the initiator of this PDMP review makes sense due to their position in the

patient’s healthcare journey and ease of access to pertinent information at the point of

dispensing. Physicians have access to the same PDMP information as pharmacists; however it

would take constant tracking of the patient after leaving the medical visit to discover aberrant

behavior. Once discovered, the physician would be in the place of reacting to prescriptions

already filled, whereas the pharmacist can screen for all prescription filling activity prior to

dispensing, and act on the spot to alert the provider and possibly avoid dispensing a prescription

that would put the patient at risk.

Purpose Statement

This study evaluates the impact of a pharmacy PDMP use policy. The researchers

compared a pharmacy that implemented a PDMP use policy with a control pharmacy given other

factors being similar (i.e., type of prescriptions filled, number of prescriptions filled) to evaluate

the impact of pre- and post-implementation of a pharmacy PDMP use policy.

This is a quantitative study of patient records from the PDMP, which models the

following over time and between intervention and control groups: (1) overutilization by

maximum morphine equivalent daily dose or maximum methadone daily dose, (2) multiple long-

acting or multiple short-acting concurrent opioid prescriptions, (3) multiple concurrent


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prescribers, (4) multiple concurrent pharmacies, (5) concurrent prescribing of an opioid and

benzodiazepine or opioid, benzodiazepine and carisoprodol, and (6) early refills from those

filling opioid prescriptions at a retail pharmacy. This data has been analyzed for differences in

each category between pre- and post implementation of the PDMP pharmacy use policy.

Research Question

At the point of opioid prescription filling, should: (1) overutilization by maximum

morphine equivalent daily dose or maximum methadone daily dose, (2) multiple long-acting or

multiple short-acting concurrent opioid prescriptions, (3) multiple concurrent prescribers; (4)

multiple concurrent pharmacies, (5) concurrent prescribing of an opioid and benzodiazepine or

opioid, benzodiazepine and carisoprodol, and (6) early refills be included as criteria used by

pharmacists accessing the PDMP to identify patient aberrant behaviors which could indicate

abuse, addiction, or increased risk of overdose and mortality?

Thesis

Criteria can be utilized by pharmacists by querying the patient in the PDMP to identify:

(1) overutilization by maximum morphine equivalent daily dose or maximum methadone daily

dose, (2) multiple long-acting or multiple short-acting concurrent opioid prescriptions, (3)

multiple concurrent prescribers, (4) multiple concurrent pharmacies, (5) concurrent prescribing

of an opioid and benzodiazepine or opioid, benzodiazepine and carisoprodol, and (6) early refills

from those filling opioid prescriptions at a retail pharmacy (OHA, 2013). These criteria are

supported by the literature as parameters to identify individuals at risk for abuse and dependence

of opioid medications and can be accessed via the PDMP at the point of dispensing by

pharmacists to monitor patient utilization of opioids. Identifying these behaviors in this way is

one way to keep these patients safe.


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Rationale for Thesis

Overutilization by dose, multiple long-acting or multiple short-acting concurrent opioid

prescriptions, multiple prescribers during overlapping time periods, multiple filling pharmacies,

concurrent prescribing of an opioid and benzodiazepine, and early refills are all criteria that can

be screened for in the Oregon Prescription Drug Database (Oregon Health Authority, 2013;

Opioid Prescribers Group, 2013). These criteria are also determinants of whether a patient is

adhering to their pain contracts, “doctor shopping,” or receiving higher than average doses of

pain-relieving opioids.

Pharmacists can access the PDMP database online at www.orpdmp.com to query

prescription filling information for controlled substances, including the patient name, patient

address, date of birth, drug filled, dose, quantity, date written, date filled, prescriber information,

and dispensing pharmacy. With this information, pharmacists have the potential to identify

individuals at risk for abuse and dependence of opioid medications by reviewing PDMP records

for these specific and identifiable criteria at the time a prescription is dispensed (Oregon Health

Authority, 2013; Opioid Prescribers Group, 2013).

Rationale for Project

Unintentional prescription-drug-related hospitalizations and deaths have risen steadily

since 2000 indicating that there is a clear safety issue that corresponds to opioid abuse

(Unintentional Prescription Drug Overdose in Oregon, 2013). With 14.7% of Oregonians ages

18-25 self-reporting that they used prescription pain relievers for non-medical purposes, there is

also a concern for misuse (Oregon Health Authority, 2013). In addition to non-medical use, the

number of Oregonians receiving “substance misuse disorder treatment” increased five-fold

between 2000 and 2011 (Oregon Health Authority, 2013). With the increased non-medical use,


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substance misuse treatment, and unintentional safety-related events, most of which are opioid

related, there is a need to look at ways to prevent misuse of opioid prescriptions.

Pharmacists are the last check before the patient receives a prescription. This presents

tremendous opportunity to implement interventions that can increase patient safety. At the

pharmacy level, data provided by the PDMP is available to help identify patients at risk

(Prescription Monitoring Program, 2013). Data supporting a PDMP use policy can result in

policy changes in the profession of pharmacy.

Summary

Since the implementation of pain management drugs, aberrant behavior relating to

controlled substances such as opioids has led to increased dependence, which can lead to deaths

due to overdose. Research has been done by many groups, and there are several sets of

guidelines for prescribers to use when prescribing these medications. Beyond the prescribing

physicians, some pharmacies have implemented a PDMP use policy, and started reviewing

patient records at the point of prescription filling, which relies on data from Oregon’s

Prescription Drug Monitoring Program (PDMP) Database. This study utilized the Oregon PDMP

by applying a set of criteria to a de-identified sample of opioid prescribed patients in an effort to

evaluate their effectiveness.


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CHAPTER 2 – LITERATURE REVIEW

Opioid Abuse

The issues of dependence and aberrant behavior pertaining to opioid use are current

concerns in journals and scientific literature. Dependence is defined in the literature as including

“a maladaptive pattern of substance use leading to clinically significant impairment or distress, as manifested by three (or more) of the following: 1) tolerance (requires increased amounts of the substance to desired effect or diminished effect with use of the same amount of the substance); 2) withdrawal (characteristic withdrawal syndrome for the substance or taking the substance to relieve or avoid withdrawal symptoms); 3) the substance is often taken in larger amounts or over a longer period than intended; 4) unsuccessful efforts to control substance use; 5) time spent on activities necessary to obtain, use, or recover from effects of the substance; 6) reduced psychosocial functioning; and 7) continued use despite known risks” (APA, 2000, p. 110).

In reference to opioids, healthcare professionals recognize that the “increased use of these

drugs has resulted in an increase in overdoses and other problems associated with drug misuse”

(Booze, 2006, p. 1). Concern in this area of healthcare has only increased as time has passed,

since prescription “misuse, abuse, addiction, overdose, and other health and social consequences

of inappropriate [prescription] use are taking a rapidly growing toll on individuals and

institutions in the United States” (Katz et al., 2013, p. 295). Something that seems consistent in

most opioid related studies is the recognition that “the behaviors of some chronic pain patients

on opioid therapy put them at greater risk of consequences often associated with addiction,

including elevated use of healthcare services, crime, and death” (Katz et al., 2013, p. 296).

There appears to be a parallel between the increase in prescribing and the increase of

abuse as is noted in multiple studies. One such study points out that “along with the increase of

prescriptions for controlled drugs from 1992 to 2003 of 154%, there was also a 90% increase in

the number of people who admitted to abusing controlled prescription drugs” (L. M.

Manchikanti, Fellows, Ailinani, & Pampati, 2010, p. 419). Similar findings from the Centers for


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Disease Control noted that the trend of increasing prescribing of opioids has closely paralleled a

rise in the numbers of overdoses (Methadone-associated overdose deaths factors contributing to

increased deaths and efforts to prevent them: Report to congressional requesters, 2009). Illegal

use of opioids has begun to outpace the abuse of non-legal drugs like cocaine in some capacities.

One study points out the “nonmedical use of prescription pain relievers is greater than

stimulants, sedatives, and tranquilizers” (Moore, Jones, Browder, Daffron, & Passik, 2009, p.

1426). Opioids can be acquired illegally by utilizing multiple pharmacies or “doctor shopping”.

Individuals can sometimes “fill prescriptions at multiple pharmacies, making them more difficult

to track. These prescriptions may be used by addicted individuals themselves, diverted to family

members or friends, or sold on the street” (Katz et al., 2013, p. 296). A screening tool can be

utilized to prevent these two activities since

“at the time of dispensing an opioid analgesic at a pharmacy, a fraud and abuse screen [can be] automatically conducted, including checks of PMP [Prescription Monitoring Program] data, validity of Drug Enforcement Administration (DEA) registrations of prescriber and pharmacist, vital status registries to ensure that both prescriber and patient are alive, and federal debarment and exclusion databases” (Katz et al., 2013, p. 296). Unsurprisingly, this healthcare issue has a substantial monetary cost as well. Studies

acknowledge the idea that “prescription opioid abuse and addiction are serious problems with

growing societal and medical costs, resulting in billions of dollars of excess costs to private and

governmental health insurers annually” (Katz et al., 2013, p. 295). The study asserts further that,

“although difficult to accurately assess, prescription opioid abuse also leads to increased

insurance costs in the form of property and liability claims, and costs to state and local

governments for judicial, emergency, and social services” (Katz et al., 2013, p. 295). The

existence of opioid related addiction, aberrant behavior, death, and cost is well documented in

the literature.


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Prescribing guidelines for chronic pain

In recent years, there has been a number of opioid treatment guidelines published. The

American Pain Society and the American Academy of Pain Medicine published a set of

guidelines in 2009 that address the use of opioid therapy for chronic non-cancer pain. At that

time the panel concluded that there was weak evidence to formulate recommendations and relied

on an expert panel. Regarding methadone, the guidelines recognize that “methadone is

characterized by complicated variable pharmacokinetics and pharmacodynamics and should be

initiated and titrated cautiously” (Chou et al., 2009, p. 118). “Starting methadone doses should

generally not exceed 30 to 40 mg a day even in patients on high doses of other opioids.” The

guidelines recognize that “more research is needed on how policies that govern prescribing affect

clinical outcomes” (Chou et al., 2009, p. 118).

Reinforcing the need for further research, the American Pain Society and American

Academy of Pain Medicine reviewed research gaps in the 2009 guideline, partly looking at

methods for monitoring opioid use and detecting aberrant drug-related behaviors. The study

confirmed the need for methadone research and reinforces the lack of evidence on benefits and

harms of high-dose opioids. It pointed out that the highest doses of morphine in trials are 240 mg

per day with the highest average dose being 120 mg per day (Chou, Ballantyne, Fanciullo, Fine,

& Miaskowski, 2009, p. 118).

Approaching the lack of evidence from another direction, the American Pain Society

points out “the United States carefully monitors controlled substance use, and probably has

liberties and privacy in the U.S. prevents the U.S. from collecting comprehensive healthcare

data across the population at large.” The study also points out that “we need evidence, not only


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to guide practice, but to inform healthcare policies and drug regulations” (Ballantyne, 2010, p.

830).

In response to the documented need for opioid related research, an Opioid

Pharmacotherapy Research Guideline was published by the American Pain Society. Among the

many areas for research that are pointed out, tool validation is essential in clinical research. The

study identifies that “no tools exist at present for specifically predicting aberrant medication

behaviors” (Chapman et al., 2010, p. 822).

In (Victor, Alvarez, & Gould, 2009, p. 1051) the emphasis on need for research is

replaced by an analysis that suggests that the “availability of pain-treatment guidelines,

recommendations, and education alone may not be enough to influence opioid-prescribing

practices in the treatment of chronic pain.” This is an important distinction that corresponded

with the idea that tools for detecting aberrant behavior are lacking (Chapman et al., 2010),

suggesting a need for intervention other than just having guidelines.

The American Society of Interventional Pain Physicians (ASIPP) advises that PDMP

screening for opioid abuse is recommended as it will identify opioid abusers and reduce opioid

abuse despite limited evidence for reliability and accuracy. The ASIPP further recommends that

prescription monitoring programs be implemented as they provide data on prescription usage,

reduce prescription drug abuse or doctor shopping (Manchikanti et al., 2012). Prescription Drug

Monitoring Program

The development a Prescription Drug Monitoring Program (PDMP) is not new to the

United States. The idea of developing a program state-wide as well as nationally comes from a

variety of studies that have been encouraged by the Obama administration in recent years. Some

studies have observed the need for such programs in populations where


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“the majority of opioid prescriptions among [a] large commercially insured population might have been appropriate, a substantial number were prescribed in a manner that suggests potential patient misuse or inappropriate prescription practice by providers” (Liu, Logan, Paulozzi, Zhang, & Jones, 2013, p. 657). In response to these types of situations, it has been largely observed that by “using

medical as well as drug claims data, it is feasible to develop models that could assist payers in

identifying patients who exhibit characteristics associated with increased risk for opioid abuse”

(Rice et al., 2012, p. 1). The idea of PDMPs are quickly gaining steam as is evident in that “48 of

the states have prescription drug monitoring programs (PDMPs), but not all of the programs are

up and running” as is outlined by journalist Kuehn. Furthermore, there are “at least 40 states now

that have operational PMPs or have enacted PMP legislation, covering 87% of the US

population, and the White House plan has committed federal resources to expanding them” (Katz

et al., 2013, p. 299). He goes on to impress the value upon his readers in stating, “such programs

can help physicians, pharmacists, and insurers identify patients who are inappropriately

accessing opioid medications, such as through obtaining prescriptions from multiple physicians

simultaneously” (Kuehn, 2012, p. 20). There is evidence to support value in both provider and

patient observation in implementing a PDMP. One study observes that “health plans may be able

to use these methods to identify members with chronic pain who exhibit similar problematic

behaviors in order to effectively intervene and optimize resource allocation” (Tkacz et al., 2013,

p. 872).

Screening categories like previous use of opioids, previous documented addictions, and

prevalence of mental health issues has largely been observed to help determine an individual’s

propensity for abuse of opioids. Some suggest that


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“using currently available screening tools designed for use in populations on or considering opioid therapy is recommended as there is evidence that patients with a prior history of drug or alcohol abuse or psychological problems are at increased risk of developing opioid related use/abuse problems” (Opioid overuse a "public health emergency", 2013, p. 103). However, the literature cautions those who would use PDMPs for anything other than

treatment decisions. One study highlights this idea in stating that “the information obtained about

drugs patients have been prescribed should be used to make therapeutic decisions but not to

punish the patients or to terminate their treatment, according to the guidelines” (AATOD: OTPs

should use PMPs but not input data... American Association for the Treatment of Opioid

Dependence... opioid treatment programs... prescription monitoring programs, 2012, p. 7).

What of the state of Oregon’s needs in this arena? In regards to previous utilization of

Prescription Monitoring Programs (PMPs) in other states, “the body of evidence, though sparse,

is robust for one assertion: PMPs reduce the use of targeted prescription medications” (Fornili &

Simoni, 2011, p. 80). Other studies are in agreement and have come to the conclusion that “even

early versions of today’s PMPs have been shown to reduce prescription drug diversion” (Katz et

al., 2013, p. 299). This assessment does not assume that all PDMPs are created equal, which they

are not. One journalist notes differences, and articulates that

“the tremendous state variability in PMP design, scope, and operationalization imposes tremendous difficulty in assessing the effectiveness of PMPs in reducing prescription medication abuse and diversion. Although several studies have empirically investigated the effectiveness of paper-based PMPs on the intended and unintended influences on controlled prescription medication use, none have evaluated electronic monitoring PMPs” (Fornili & Simoni, 2011, p. 79).

The idea that all PDMPs are not created equal gives credence to the idea that there needs

to be much more evaluation of programs on an individual basis. One author asserts, “future

studies to evaluate the impact of PMPs on medical use and access, abuse and diversion, and

clinical and economic outcomes are imperative.” They continue along that vain in stating


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“PMP outcome measures are needed to fully evaluate the impact on patients along with drug abuse and diversion. The goal of balancing appropriate treatment with reductions in prescription drug abuse and diversion should remain in the forefront of new and alternative drug policy strategies” (Fornili & Simoni, 2011, p. 81).

Prescribing Guidelines – Dosing Limits

The American Society of Interventional Pain Physicians (ASIPP) recommends

stratification of patients into low-, medium-, and high-risk categories for dosing. “Up to 40 mg of

morphine equivalent [per day] is considered as low dose, 41-90 mg of morphine equivalent [per

day] as moderate dose, and greater than 91 mg morphine equivalent [per day] as high dose”

(Manchikanti et al., 2012, p. S68).

The Southern Oregon Opioid Prescribing Guidelines are in agreement with the ASIPP,

with a slightly different dose/risk stratification. The Opioid Prescriber’s Group (OPG)

recommends keeping doses of opioids below 120 mg Morphine Equivalent Dose (MED) per day.

Doses of greater than 100 mg MED per day have a nine-fold increase risk in death over low-dose

morphine. The guidelines recommend that methadone doses generally not exceed 40 mg daily.

The guidelines classify patients as high risk if they take greater than 100 mg MED per day or 40

mg methadone per day, moderate risk if they take 41-100 MED per day, and low risk if they take

40 MED per day or less. The guidelines point out that many experts advise against concomitant

use of benzodiazepines and opioids because of synergistic effects resulting in respiratory

depression (OPG, 2013).

McCarthy (2012) is in agreement with the Opioid Prescriber’s Group (OPG, 2013). The

State of Washington, when revising the guidelines issued in 2007, designated an “opioid dose

threshold of 120 mg/day” morphine equivalent dose. “At that dose, the guidelines advised, if the

patient’s pain and functionality had not substantially improved, the physician should consult with

a pain specialist before increasing the dose” (McCarthy, 2012, p. 1).


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Another study builds on previous research in Washington looking at the workers

compensation population, again recommending a dose cap of 120 mg/day MED. The study

looked at periods before and after disseminating the Washington Opioid Dosing Guideline and

found that a

“yellow flag dosing threshold [greater than 120 mg/day] may be a useful tool for improving the safety of opioid prescribing practices by discouraging further dose escalation. Both chronic and high-dose opioid use rates declined among incident users after the Washington Guideline implementation” (Garg et al., 2013, p. 1620). In Preventing Prescription Opioid Overdose there is a section of the review that

evaluates the avoidance of dangerous drug combinations. In agreement with the Southern

Oregon Opioid Prescribing Guidelines (OPG, 2013), “opioid co-administration with other central

nervous system depressants, particularly alcohol and benzodiazepines significantly increases

morbidity and mortality.” In agreement with methadone dosing guidelines and in response to an

increase in methadone-related adverse events, the U.S. Drug Enforcement Administration (DEA)

and drug manufacturers “agreed to limit the distribution of methadone 40 mg dispersible tablets

to only those facilities authorized for treatment of opioid addiction” (Morgan & Weaver, 2010,

p. 512).

Risk for Overdose from Methadone Use for Pain Relief states that

“interventions such as the use of prescription drug monitoring programs, appropriate screening and monitoring before prescribing opioid pain relievers, regulatory and law enforcement efforts, and state policies aimed at providers and patients involved in diversion of these drugs continue to be essential elements in addressing this public health emergency” (Vital signs: Risk for overdose from methadone used for pain relief - United States, 1999-2010, 2012, p. 496). (Gudin, Mogali, Jones, & Comer, 2013, p. 118) concludes that “there is a defined

increase in rates of adverse events, overdose, and death when these agents,” referring to opioids,

benzodiazepines, and alcohol are used in combination. “Urine testing and Prescription


14

Monitoring Programs are two indispensable tools that can identify patients who are non-adherent

to treatment, have filled multiple prescriptions at multiple pharmacies, and/or are abusing

prescription drugs and/or illicit drugs.”

Further support for the risks of opioids and benzodiazepines use together is seen in an

editorial by Webster, 2010, p. 801). “The fact that benzodiazepines and opioids have frequent

appearances as co-intoxicants in opioid-related deaths presents yet another risk” which is

compounded by the fact that “In 2006, about half of all U.S. opioid related deaths involved more

than one type of drug with benzodiazepines mentioned most frequently.”

Screening Criteria

The literature addressing opioid prescription screening tools references a screening tool

with a

“prescription abuse checklist of five criteria: overwhelming focus on opiate uses, pattern of early refills, multiple telephone calls or unscheduled visits, episodes of lost or stolen prescriptions, and supplemental sources of opioids. Of these criteria, patterns of early refills and supplemental sources (e.g., from other prescribers) of opioids could be identified in a prescription monitoring program database” (Butler et al., 2004, p. 65).

(Chabal et al., 1997) took the five criteria described in (Butler et al., 2004) and applied

them to patients enrolled in a pain clinic. It was found that “the criteria had good reliability and

can be applied during normal clinical interactions.” This provides additional validation for those

criteria that can be tested in a PMP.

Reinforcing some of the criteria for screening in the Butler study, a study was completed

on the Michigan Medicaid population on patients identified as having three or more prescriptions

from two or more providers in six months. The study looked at evidence of medication

management agreements, early refills, increasing doses, telephone call frequency, reports of lost

or stolen medications, history of abuse, and medical chart documentation. It was found that


15

concurrent use of non-opioid analgesics, escalating opioid dosage and number of providers best

predicted the number of opioid prescriptions. This study confirms the need for dosage review

and also introduces the concept of multiple prescribers being correlated to opioid use.

Expanding on the concept of multiple providers, a West Virginia study compared live

subjects with persons who died due to drug overdose. The study found that a significantly greater

proportion of deceased subjects were “doctor shoppers” and “pharmacy shoppers” than were the

live subjects. Doctor shopping was defined as having received prescriptions from three or more

clinicians during the six months prior to death. Pharmacy shopping was defined as filling

prescriptions in four or more pharmacies in the same time frame. This study further validates

looking at multiple prescribers and pharmacies in prevention of deaths due to drug overdose,

these being criteria that can be searched in a prescription monitoring database.

Doctor shopping is a conceptual theme that shows up throughout the literature. (Julie &

Hall, 2012) compiled an analysis on the topic and raised a number of issues related to doctor

shopping such as economic costs, ethical issues, legal ramifications from those who benefit, as

well as risk to patients. They point out that a “positive consequence of doctor shopping has

occurred in the professional and legal sectors wherein Prescription Drug Monitoring Programs

have helped to “catch” instances of doctor shopping and would relieve the prescriber of veritably

needing to have a “crystal ball” to determine who may be deceiving them or not.”

Barriers in Implementing Screening Tools

Some barriers exist in regards to the implementation of PDMPs. One rather large

stumbling block exists in the financial impetus required to move forward for many states. Some

administrative experts have noted that


16

“although prescription drug monitoring programs are designed and maintained at the discretion of each state, federal funds from the Bureau of Justice have assisted in facilitating interstate data exchange and in creating and expanding data collection and analysis systems” (Gugelmann & Perrone, 2011, p. 2258). They also offer more insight by outlining additional barriers in the form of interstate data

communication. They mention how important this aspect of a system is in stating that the

“function and success of drug monitoring programs continue to be limited by variability in data collection systems and the interstate exchange of information. New interstate collaboration initiatives through the National Association of Boards of Pharmacy and the Alliance of States with Prescription Monitoring Programs attempt to address these issues” (Gugelmann & Perrone, 2011, p. 2258).

The cost to payers is another financial burden to add to the list when discussing barriers

to implementing PDMPs. The time spent for healthcare professionals to use such screening tools

is also a cost-related concern. One study that focuses on mitigating risks for payers states that,

“in the future, they could make it easier for time-challenged physicians to use these data by

linking the PMP to their electronic prescribing tools, or to pharmacy computers at the time of

dispensing” (Katz et al., 2013, p. 299).

Others in the field identify lack of knowledge in relation to identifying drug-related

behaviors and risk factors. They conclude that

“What is truly needed (and indeed it is glaring that such a study has not yet been carried out) are large epidemiologic surveys of risk factors evaluated prospectively as patients enter into treatment and are then followed for a meaningful time period (i.e., 1-year minimum, if not longer). The conclusions from such studies would be extremely valuable to practicing clinicians in that they could be employed not to justify excluding patients from treatment with opioids in the face of severe pain, but to plan for and put in place safeguards and structures in the treatment approach that would potentially enable such patients to enjoy good outcomes.” (Passik & Kirsh, 2003, p. 186).

Some of the literature on opioid prescribing limitations suggests that physicians are

concerned that the use of screening tools may limit their ability to offer the kind of treatment

they aspire to provide. One article iterates this idea by stating that “pain physicians are concerned


17

that there will be a backlash against their work and their patients, resulting in a return to under

treatment of pain” (Wiley, 2007, p. 5).


18

CHAPTER 3 - METHODOLOGY

Introduction

This chapter will cover the methodology of this study, including the research questions being

answered, the setting of the research, the study design, research methods, how findings will be

reported, and ethical considerations.

This study evaluates impact of a pharmacy PDMP use policy. The researchers compared

a pharmacy that implemented a PDMP use policy with a control pharmacy, given other factors

being similar (i.e., type of prescriptions, number of prescriptions filled annually) to evaluate the

impact of pre- and post-implementation of a pharmacy PDMP use policy.

This is a quantitative study of patient records in the PDMP database utilizing opioids for

pain management by comparing the following during pre- and post-implementation, and between

groups: (1) overutilization by maximum morphine equivalent daily dose or maximum methadone

daily dose, (2) multiple long-acting or multiple short-acting concurrent opioid prescriptions, (3)

multiple concurrent prescribers, (4) multiple concurrent pharmacies, (5) concurrent prescribing

of an opioid and benzodiazepine or opioid, benzodiazepine and carisoprodol and (6) early refills

from those filling opioid prescriptions at a retail pharmacy. These data have been analyzed for

correlations between each category and prior to and after implementation of the PDMP

pharmacy use policy.

Research Methodology

This research is quantitative in nature. To understand why the quantitative methodology

is the best for recommending criteria for inclusion in a PDMP used policy, it helps to look at

what the qualitative method would provide as an alternative approach. In the qualitative method

the researcher would start with interviews or observations, ask open-ended questions, form


19

categories or themes from the generalizations or broad patterns, and finally pose theories from

past experience or literature (Creswell, 2003, p. 63). This methodology would not work well

because this research is not looking for theories or patterns; there is a body of known information

and a specific problem that requires hard evidence for use in validating specific criteria. There is

also medical literature that will support already determined criteria. This study is not trying to

deduce a theory but rather validate criteria based on information already known. (Creswell,

2003) describes quantitative as “measuring variables using an instrument to obtain scores,”

which is the purpose of this study (Creswell, 2003, p. 57).

Research Methods

Sample Eligibility Criteria

The sample records in this study are from a de-identified PDMP flat-file data of adults

who filled a prescription at the retail (intervention) pharmacy between the date ranges of July 1,

2012 and June 30, 2013, which represents pre-implementation of a pharmacy PDMP use policy.

The date ranges of July 1, 2013 and June 30, 2014 were analyzed for patients filling opioid

prescriptions post-implementation of a pharmacy PDMP use policy.

Sample Exclusion Criteria

This study does not include patient records of children, minors or prisoners. Records of

patients 89 years of age or older have been excluded. The data collected does not provide

information for identifying if a patient is cognitively impaired, or in any other protected

population, however these patient records are not being selected specifically for inclusion in the

study. Tramadol has not been considered an opioid for the purposes of this study, as it is not

included in the PDMP database. Hospice patient records have been excluded from this study due

to use of doses of opioids outside of the threshold of chronic pain patients.


20

Data Collection

The PDMP has a business need to evaluate the impact of the data system information on

patient care and community health outcomes. The PDMP statute ORS 431.966(1)(b), allows

PDMP de-identified data to be used for research. The researchers utilized an encrypted,

password protected portable hard drive, which was allowed to leave OHA premises for data

analysis after all PDMP data elements were removed.

The researchers conducted a visual analysis of the de-identified PDMP flat file data,

utilizing a PDMP cluster ID (a number assigned by the system which links patients based on

algorithms using patient name, DOB, and patient address). A statistician adjunct faculty member

of Pacific University has been consulted regarding analysis of the data.

The data has been analyzed using the following:

Maximum Morphine Equivalent Daily Dose

For each patient, the maximum morphine equivalent dose was recorded. The proportion

of patient’s taking >120 mg morphine equivalent dose per day as well as average maximum

morphine equivalent daily dose for the population sample have been calculated. Only

prescriptions filled at the intervention and control pharmacies were used. Patients with a filling

history that indicates hospice use (liquid forms, belladonna, etc.) were excluded and methadone

was excluded as it is analyzed separately. Only patients with at least one long-acting opioid,

indicating chronic pain treatment, were included.

Maximum Methadone Daily Dose

For each patient, the maximum methadone daily dose was recorded. The proportion of

patient’s taking >40 mg methadone per day, as well as the average maximum methadone daily


21

dose for the population sample, have been calculated. This included only prescriptions filled at

the intervention and control pharmacies and only patients on methadone.

Concurrent Benzodiazepines and Opioids

The proportion of patients with concurrent prescriptions for both benzodiazepines and

opioids was recorded. This analysis used prescriptions filled at the intervention and control

pharmacies only. We identified patients on benzodiazepines or opioids and then looked for the

other concurrent benzodiazepine or opioid prescriptions during the time period.

Concurrent Benzodiazepines, Opioids, & Carisoprodol

The proportion of patients with concurrent prescriptions for benzodiazepines, opioids and

carisoprodol was determined. This included prescriptions filled at the intervention and control

pharmacies only. We identified patients on carisoprodol and then looked for both concurrent

opioids and benzodiazepines in addition to the carisoprodol.

Multiple Concurrent Prescribers

The proportion of patients with multiple concurrent prescriptions for opioids filled and

written by separate providers during a concurrent time period was determined. We also recorded

the number of times the patient filled a prescription using multiple concurrent prescribers. We

used patients at the intervention and control pharmacies to find a random patient sample with the

opportunity and intervention available to them. We then analyzed the sample in the global

database to look for multiple concurrent prescribers defined as filling a prescription for an opioid

when filling a prescription written by another prescriber in the last 30 days for an opioid. If the

time period of filling for another prescriber was >30 days and no days supply exceeded that time

frame, it was assumed that the patient had switched providers. We included only patients with a


22

significant filling history (>10 prescriptions filled) in the pre- or post-implementation time

period.

Multiple Concurrent Pharmacies

The proportion of patients with multiple concurrent prescriptions for opioids filled at

separate pharmacies during a concurrent time period was determined. We also recorded the

number of times the patient filled a prescription using multiple concurrent pharmacies. We used

patients at the intervention and control pharmacies to find a random patient sample with the

opportunity and intervention available to them. We then analyzed the sample in the global

database to look for multiple concurrent pharmacies defined as filling a prescription for an

opioid, when at another pharmacy in the last 30 days the patient filled for an opioid. If the time

period of filling at another pharmacy was >30 days and no days supply exceeded that time frame,

it was assumed that the patient had switched pharmacies. We only included patients with a

significant filling history (>10 prescriptions) filled in the pre- or post-time period.

Multiple Concurrent Long-Acting, Short-Acting Opioids

Due to limitations in the data set (missing days supply), the number of occurrences of

more than one concurrent long-acting opioid prescription and more than one concurrent short-

acting opioid prescription were not recorded.

Early Refills

Due to limitations in the data set (missing days supply), early refill analysis was not

conducted.


23

Data Analysis


The data set is comprised of binomial data where each patient either did or did not have a

maximum daily morphine equivalent dose of >120 mg. It is also comprised of continuous data

with the actual calculated daily dose recorded. The binomial data was analyzed using a chi

square (Χ2) test for statistically significant differences. We calculated variances for the sample

population doses. We utilized the F-test to determine whether the variance was due to chance or

the intervention between pre- and post-implementation. An F-test resulting <0.05 was considered

unequal variance and likely due to the intervention. Depending on whether variances were

determined to be equal or unequal, we used the corresponding t-test to determine statistical

significance between maximum morphine equivalent average doses. All of these analyses were

performed for the intervention and control data sets.


The data set is comprised of binomial data where each patient either did or did not have a

maximum daily methadone dose of >40 mg. It is also comprised of continuous data with the

actual daily dose recorded. The binomial data was analyzed using a chi square (Χ2) test for

statistically significant differences. We calculated variances for the sample population doses. We

utilized the F-test to determine whether the variance was due to chance or the intervention

between pre- and post-implementation. An F-test resulting <0.05 was considered unequal

variance and likely due to the intervention. Depending on whether variances were determined to

be equal or unequal, we used the corresponding t-test to determine statistical significance

between maximum morphine equivalent average doses. All of these analyses were performed for

the intervention and control data sets.


24


The data set is comprised of binomial data where each patient either did or did not have

concurrent benzodiazepine and opioid prescriptions. This binomial data was analyzed using a chi

square Χ2 test for statistically significant differences.

Concurrent Benzodiazepines, Opioids, & Carisoprodol

The data set is comprised of binomial data where each patient either did or did not have

concurrent benzodiazepines, opioids and carisoprodol prescriptions. This binomial data was

analyzed using a chi square (Χ2) test for statistically significant differences.


The data set is comprised of binomial data where each patient either did or did not utilize

multiple concurrent prescribers. It is also comprised of integer data with the number of

occurrences recorded. The binomial data was analyzed using a chi square (Χ2) test for statistically

significant differences. Integer data was plotted for inference.


The data set is comprised of binomial data where each patient either did or did not utilize

multiple concurrent pharmacies. It is also comprised of integer data with the number of

occurrences recorded. The binomial data was analyzed using a chi square (Χ2) test for statistically

significant differences. Integer data was plotted for inference.

Multiple Concurrent Long-Acting, Short-Acting Opioids

Due to limitations in the data set (missing days supplies), the number of occurrences of

more than one concurrent long-acting opioid prescription and more than one concurrent short-

acting opioid prescription were not analyzed.


25

Early Refills

Due to limitations in the data set (missing days supplies), early refill analysis was not

conducted.

Data Security and Handling

Data was stored on an encrypted, password-protected portable hard drive while it

contained any de-identified PDMP flat-file data. After all PDMP de-identified flat-file data

elements were removed, it remained on the encrypted, password protected portable hard drive,

and was permitted to leave OHA premises for data analysis. At the conclusion of the study all

data was destroyed by the researchers by repartitioning and reformatting the hard drive.

Reporting Findings

Results have been presented, in partial fulfillment of the Masters of Healthcare

Administration degree, to the Oregon Board of Pharmacy (OBOP), the sponsoring pharmacy, the

PDMP, and at Pacific University. A bound copy remains in the offices of the Pacific University

Masters of Healthcare Administration Program and may be submitted for further publication.

Ethical Considerations

As we endeavor to portray the value of this PDMP utilization, which aims to improve the

safety of opioid utilizers, it is important that we as researchers, “respect the participants and the

sites for research” (Creswell, 2003, p. 89). The nature of the research that was conducted for

evaluating the PDMP use policy does not include “vulnerable populations such as minors (under

the age of 18), mentally incompetent participants, victims, persons with neurological

impairments, pregnant women or fetuses, prisoners, and individuals with AIDS” (Creswell,

2003, p. 89). Despite the exclusion of these vulnerable populations, there are some ethical

considerations.


26

One crucial ethical consideration is

“gaining the agreement of individuals in authority (e.g., gatekeepers) to provide access to de-identified data at research sites. This often involves writing a letter that identifies the extent of time, the potential impact, and the outcomes of research” (Creswell, 2003, p. 90).

Pacific University and the sponsoring institution, the “gatekeepers” in this instance, have

taken steps to ensure that both members of the research team had appropriate access to the

patient data at the sponsoring institution. The sponsoring institution has granted this study’s

researchers association via the Affiliation Agreement for student experience under the current

contract with Pacific University. This document lists the expectations of the researchers and their

role as students. The researchers have followed the sponsoring institution’s policies and

guidelines. Additionally, an Oregon Prescription Drug Monitoring Program Data Use Agreement

has been approved and followed by the researchers of this study.

Another ethical consideration “arises when there is not reciprocity between researcher

and the participants” (Creswell, 2003, p. 90). In the instance of this research, the need for

evaluating the PDMP utilization policy as it was addressed in the literature review highlights the

fact that this tool, if it is effective, will serve to benefit individuals who require opioids in their

day-to-day lives by identifying risk factors that can be reviewed by a pharmacist and

communicated to a prescribing physician in an effort to ensure the patient’s safety. The PDMP

use policy is to be used to minimize aberrant behavior and deaths due to overdose. This

motivation is certainly in the best interest of the research subject. Furthermore, the outcome of

this research has ramifications for more patients than those utilized for this study. Validation of

this PDMP use policy could potentially make opioid use safer for all of Oregon if utilized

statewide.


27

The social stigma associated with pain management medication creates an ethical

consideration that must be taken into consideration. It is important that researchers “anticipate

the possibility of harmful, intimate information being disclosed during the data collection

process” (Creswell, 2003, p. 91). To this end, data that contained identifiers were not provided to

the researchers by the Oregon Health Authority.

This document does not “use language or words that are biased against persons because

of gender, sexual orientation, racial or ethnic group, disability, or age” (Creswell, 2003, p. 92).

The data provided does not contain any information about sexual orientation, racial or ethnic

group, or disability of participants.

Summary

In summary, we have discussed in detail the methodology of this study, which includes

the research questions being answered, the setting of the research, the study design, research

methods, how we have reported findings, and ethical considerations. This methodology has been

use to answer the following question; should the criteria at the point of opioid prescription filling

be used by pharmacists accessing the PDMD to identify unsafe prescribing practices and patient

aberrant behaviors which could indicate abuse, addiction, or increased risk of overdose and

mortality?

In an effort to answer these questions, this research study has adopted a quantitative

approach. This is because there is a body of known information and a specific problem with hard

evidence to use in validating specific criteria. The criteria evaluated are: (1) overutilization by

maximum morphine equivalent daily dose or maximum methadone daily dose, (2) multiple long-

acting or multiple short-acting concurrent opioid prescriptions, (3) multiple concurrent

prescribers, (4) multiple concurrent pharmacies, (5) concurrent prescribing of an opioid and


28

benzodiazepine or opioid, benzodiazepine and carisoprodol, and (6) early refills from those

filling opioid prescriptions at a retail pharmacy.

The aforementioned techniques have been used for analyzing and presenting the data,

which the literature suggests may indicate aberrant behavior on the patient’s part and put the

patient at risk for overdose or mortality.


29

CHAPTER 4 - RESULTS


Table 1 and Table 2 are each composed of three columns pre-implementation and post-

implementation of the PDMP use policy. The “Cluster ID” column is a numbering the patient

samples collected. We have called it a cluster ID because it represents a cluster of records

pertaining to the pre-implementation or post-implementation time period. The “Max MEq”

column is the maximum morphine equivalent daily dose. The “>120 mg (Y/N)” column denotes

whether the dose in the “Max MEq column” is greater than 120 mg. Table 3 and Table 4

summarize the “>120 mg (Y/N)” data for the intervention and control groups respectively for

statistical analysis. Figure 1 and Figure 2 graphically represent Table 3 and Table 4 respectively.


30

Table 1: Max Morphine Equivalent Daily Dose (mg) at the Intervention Pharmacy

Pre-Implementation

Post-Implementation Average 116

Average 101

# Yes 21

# Yes 13 # No 29

# No 37

Cluster ID

Max MEq

>120 mg

(Y/N)

Cluster ID

Max MEq

>120 mg

(Y/N) 1 90 N

1 70 N

2 230 Y

2 60 N 3 62 N

3 100 N

4 116 N

4 60 N 5 90 N

5 70 N

6 60 N

6 45 N 7 145 Y

7 52.5 N

8 180 Y

8 180 Y 9 150 Y

9 50 N

10 180 Y

10 180 Y 11 120 N

11 154 Y

12 140 Y

12 50 N 13 154 Y

13 120 N

14 110 N

14 40 N 15 160 Y

15 60 N

16 120 N

16 180 Y 17 30 N

17 60 N

18 200 Y

18 60 N 19 180 Y

19 60 N

20 170 Y

20 70 N 21 90 N

21 60 N

22 92 N

22 66 N 23 145 Y

23 100 N

24 158 Y

24 150 Y 25 100 N

25 75 N

26 150 Y

26 15 N 27 75 N

27 50 N

28 30 N

28 60 N 29 90 N

29 100 N

30 50 N

30 75 N 31 30 N

31 30 N

32 83 N

32 60 N 33 45 N

33 146 Y

34 196 Y

34 165 Y 35 165 Y

35 75 N

36 190 Y

36 90 N 37 60 N

37 50 N

38 30 N

38 50 N 39 30 N

39 240 Y

40 200 Y

40 200 Y 41 50 N

41 105 N

42 135 Y

42 180 Y 43 50 N

43 300 Y

44 50 N

44 57.5 N 45 50 N

45 50 N

46 46 N

46 46 N 47 110 N

47 110 N

48 330 Y

48 330 Y 49 200 Y

49 255 Y

50 60 N

50 45 N


31

Table 2: Maximum Morphine Equivalent Daily Dose (mg) at the Control Pharmacy

Pre-Implementation


Average 139

# Yes 27

# Yes 25 # No 24

# No 26

Cluster ID

Max MEq

>120 mg

(Y/N)

Cluster ID

Max MEq

>120 mg

(Y/N) 1 330 Y

1 60 N

2 54 N

2 330 Y 3 240 Y

3 20 N

4 60 N

4 90 N 5 225 Y

5 60 N

6 175 Y

6 54 N 7 165 Y

7 180 Y

8 130 Y

8 165 Y 9 220 Y

9 20 N

10 120 N

10 120 N 11 90 N

11 75 N

12 120 N

12 290 Y 13 390 Y

13 34 N

14 90 N

14 120 N 15 150 Y

15 360 Y

16 90 N

16 60 N 17 120 N

17 110 N

18 360 Y

18 180 Y 19 150 Y

19 180 Y

20 140 Y

20 118 N 21 100 N

21 30 N

22 90 N

22 180 Y 23 135 Y

23 210 Y

24 140 Y

24 150 Y 25 45 N

25 120 N

26 84 N

26 90 N 27 60 N

27 95 N

28 45 N

28 200 Y 29 180 Y

29 135 Y

30 85 N

30 140 Y 31 45 N

31 150 Y

32 200 Y

32 202.5 Y 33 45 N

33 195 Y

34 45 N

34 154 Y 35 180 Y

35 200 Y

36 135 Y

36 115 N 37 140 Y

37 100 N

38 90 N

38 90 N 39 42.5 N

39 120 N

40 150 Y

40 75 N 41 120 N

41 150 Y

42 200 Y

42 180 Y 43 202.5 Y

43 225 Y

44 195 Y

44 180 Y 45 180 Y

45 60 N

46 65 N

46 30 N 47 300 Y

47 220 Y

48 195 Y

48 225 Y 49 105 N

49 90 N

50 210 Y

50 50 N 51 30 N

51 280 Y


32

Table 3: Results Summary – Maximum Morphine Equivalent Daily Dose (Intervention)

Outcome Pre Post Total >120 mg 21 13 34 <120 mg 29 37 66

Total 50 50

Sample Size: 100

Pre-Implementation: Variance = 4,223, Standard Deviation = 65

Post-Implementation: Variance = 5,068, Standard Deviation = 71

F-test: 0.53

Χ2: (p = 0.022)

t-test: (p = 0.15) (equal variance, 1-tailed, paired)

Figure 1: % Patients >120 mg Morphine Equivalent Daily Dose (Intervention)

0%

10%

20%

30%

40%

50%

60%

Pre Post

42% 26% Pe

rcentage

% Patients >120 mg Morphine Equivalent Daily Dose (Intervention)


33

Table 4: Results Summary – Maximum Morphine Equivalent Daily Dose (Control)

Outcome # Pre # Post Total >120 mg 27 25 52 <120 mg 24 26 50

Total 51 51

Sample Size: 102



F-test: 0.72

Χ2: (p = 0.57)

t-test: (p = 0.41) (equal variance, 1-tailed, paired)

Figure 2: % Patients >120 mg Morphine Equivalent Daily Dose (Control)

0%

10%

20%

30%

40%

50%

60%

Pre Post

53% 49%

Percentage

% Patients Taking >120 mg Morphine Equivalent Daily Dose (Control)


34

Maximum Daily Methadone Dose >40 mg Table 5 and Table 6 are each composed of three columns pre-implementation and post-



pertaining to the pre-implementation or post-implementation time period. The “Max Daily

Methadone (mg)” column is the maximum methadone daily dose. The “>40 mg (Y/N)” column

denotes whether the dose in the “Max Daily Methadone (mg)” is greater than 40 mg. Table 7 and

Table 8 summarize the “>40 mg (Y/N)” data for the intervention and control groups respectively

for statistical analysis. Figure 3 and Figure 4 graphically represent Table 7 and Table 8

respectively.


35

Table 5: Maximum Methadone Daily Dose (mg) at the Intervention Pharmacy

Pre-Implementation


Average 19

# Yes 7

# Yes 2 # No 9

# No 17

Cluster ID

Max Daily Methadone

(mg) >40 mg (Y/N)

Cluster ID

Max Daily Methadone

(mg) >40 mg (Y/N)

1 30 N

1 10 N 2 130 Y

2 40 N

3 10 N

3 15 N 4 15 N

4 10 N

5 5 N

5 10 N 6 60 Y

6 15 N

7 80 Y

7 20 N 8 70 Y

8 10 N

9 60 Y

9 10 N 10 40 N

10 30 N

11 20 N

11 60 Y 12 90 Y

12 10 N

13 10 N

13 10 N 14 40 N

14 45 Y

15 5 N

15 5 N 16 50 Y

16 10 N

17 40 N

18 5 N

19 10 N


36

Table 6: Maximum Methadone Daily Dose (mg) at the Control Pharmacy

Pre-Implementation


Average 69

# Yes 21

# Yes 19 # No 11

# No 11

Cluster ID Max Daily Methadone

(mg) >40 mg (Y/N)

Cluster ID Max Daily Methadone

(mg) >40 mg (Y/N)

1 80 Y

1 80 Y 2 60 Y

2 20 N

3 100 Y

3 7.5 N 4 10 N

4 280 Y

5 80 Y

5 60 Y 6 60 Y

6 90 Y

7 280 Y

7 90 Y 8 40 N

8 60 Y

9 40 N

9 70 Y 10 90 Y

10 120 Y

11 50 Y

11 120 Y 12 60 Y

12 90 Y

13 120 Y

13 40 N 14 90 Y

14 80 Y

15 60 Y

15 130 Y 16 80 Y

16 5 N

17 130 Y

17 15 N 18 15 N

18 15 N

19 5 N

19 40 N 20 80 Y

20 60 Y

21 30 N

21 30 N 22 20 N

22 50 Y

23 50 Y

23 15 N 24 15 N

24 150 Y

25 150 Y

25 60 Y 26 60 Y

26 35 N

27 35 N

27 120 Y 28 160 Y

28 80 Y

29 90 Y

29 10 N 30 10 N

30 60 Y

31 60 Y 32 30 N


37

Table 7: Results Summary: Maximum Methadone Daily Dose (Intervention)

Outcome Pre Post Total >40 mg 7 2 9 <40 mg 9 17 26 Total 16 19

Sample Size: 35


Post-Implementation: Variance = 251, Standard Deviation = 16

F-test: 0.0016

Χ2: (p = 0.0011)

t-test: (p = 0.0077) (unequal variances, 1-tailed, paired)

Figure 3: % Patients >40 mg Methadone Daily Dose (Intervention)

0% 10% 20% 30% 40% 50% 60% 70%

Pre Post

44%

11%

Percentage

% Patients >40 mg Methadone Daily Dose (Intervention)


38

Table 8: Results Summary: Maximum Methadone Daily Dose (Control)

Outcome Pre Post Total >40 mg 21 19 40 <40 mg 11 11 22 Total 32 30

Sample Size: 62



F-test: 0.92

Χ2: (p = 0.66)

t-test: (p = 0.48) (equal variances, 1-tailed, paired)

Figure 4: % >40 mg Methadone Daily Dose (Control)

0% 10% 20% 30% 40% 50% 60% 70%

Pre Post

66% 63%

Percentage

% Patients >40 mg Methadone Daily Dose (Control)


39


Table 9 and Table 10 are each composed of two columns pre-implementation and post-



pertaining to the pre-implementation or post-implementation time period. The “Occur. BZD +

Op” column denotes whether there were concurrent benzodiazepines and opioids observed.

Table 11 and Table 12 summarize the “Occur. BZD + Op” data for the intervention and control

groups respectively for statistical analysis. Figure 5 and Figure 6 graphically represent Table 11

and Table 12 respectively.


40

Table 9: Concurrent Benzodiazepines and Opioids at the Intervention Pharmacy

Pre-Implementation

Post-Implementation # Yes 22

# Yes 19

# No 28

# No 31 Cluster

ID Occur.

BZD + Op

Cluster ID

Occur. BZD + Op

1 Y

1 N 2 Y

2 N

3 Y

3 N 4 N

4 N

5 N

5 Y 6 N

6 Y

7 N

7 Y 8 N

8 N

9 N

9 N 10 Y

10 N

11 N

11 Y 12 N

12 N

13 N

13 N 14 N

14 Y

15 N

15 Y 16 N

16 Y

17 N

17 Y 18 N

18 Y

19 N

19 Y 20 Y

20 N

21 Y

21 N 22 Y

22 Y

23 N

23 N 24 Y

24 N

25 N

25 Y 26 Y

26 Y

27 Y

27 Y 28 Y

28 Y

29 Y

29 N 30 Y

30 N

31 Y

31 Y 32 N

32 N

33 N

33 N 34 Y

34 N

35 N

35 Y 36 N

36 Y

37 Y

37 N 38 N

38 N

39 N

39 N 40 Y

40 N

41 Y

41 N 42 N

42 N

43 Y

43 N 44 Y

44 N

45 Y

45 N 46 N

46 N

47 N

47 Y 48 Y

48 N

49 N

49 N 50 N

50 N


41

Table 10: Concurrent Benzodiazepines and Opioids at the Control Pharmacy

Pre-Implementation


# Yes 21

# No 28

# No 29 Cluster

ID Occur.

BZD + Op

Cluster ID

Occur. BZD + Op

1 N

1 Y 2 N

2 N

3 N

3 Y 4 N

4 Y

5 N

5 Y 6 Y

6 N

7 Y

7 Y 8 Y

8 Y

9 Y

9 N 10 Y

10 Y

11 Y

11 N 12 Y

12 N

13 Y

13 Y 14 Y

14 N

15 N

15 Y 16 N

16 Y

17 N

17 Y 18 Y

18 N

19 N

19 N 20 Y

20 N

21 Y

21 Y 22 N

22 N

23 N

23 N 24 Y

24 N

25 N

25 N 26 N

26 Y

27 Y

27 Y 28 N

28 N

29 N

29 N 30 N

30 Y

31 Y

31 N 32 Y

32 N

33 Y

33 N 34 N

34 Y

35 N

35 Y 36 N

36 Y

37 N

37 N 38 Y

38 N

39 Y

39 N 40 N

40 Y

41 N

41 Y 42 N

42 N

43 Y

43 N 44 N

44 N

45 Y

45 N 46 Y

46 Y

47 N

47 N 48 N

48 N

49 N

49 N 50 N

50 N


42

Table 11: Results Summary - Concurrent Benzodiazepines and Opioids (Intervention)

Outcome Pre Post Total Yes 22 19 41 No 28 31 59

Total 50 50

Sample Size: 100

Χ2: (p = 0.39)

Figure 5: % Patients on Concurrent Benzodiazepines and Opioids (Intervention)

0%

10%

20%

30%

40%

50%

Pre Post

44% 38%

Percentage

% Patients on Concurrent Benzodiazepines and Opioids (Intervention)


43

Table 12: Results Summary - Concurrent Benzodiazepines and Opioids (Control)


Total 50 50

Sample Size: 100

Χ2: (p = 0.78)

Figure 6: % Patients on Concurrent Benzodiazepines and Opioids (Control)

0%

10%

20%

30%

40%

50%

Pre Post

44% 42%

Percentage

% Patients on Concurrent Benzodiazepines and Opioids (Control)


44

Concurrent Benzodiazepines, Opioids, and Carisoprodol

Table 13 and Table 14 are each composed of two columns pre-implementation and post-



pertaining to the pre-implementation or post-implementation time period. The “Triple Combo”

column denotes whether there were concurrent benzodiazepines, opioids, and carisoprodol

observed. Table 15 and Table 16 summarize the “Triple Combo” data for the intervention and

control groups respectively for statistical analysis. Figure 7 and Figure 8 graphically represent

Table 15 and Table 16 respectively.


45

Table 13: Concurrent Benzos, Opioids, and Carisoprodol at the Intervention Pharmacy

Pre-Implementation


# Yes 8

# No 42

# No 48 Cluster

ID Triple Combo

Cluster ID

Triple Combo

1 N

1 Y 2 Y

2 Y

3 Y

3 N 4 Y

4 N

5 Y

5 Y 6 N

6 N

7 Y

7 N 8 Y

8 N

9 N

9 N 10 Y

10 N

11 Y

11 N 12 Y

12 Y

13 Y

13 Y 14 N

14 N

15 N

15 N 16 N

16 N

17 Y

17 N 18 N

18 N

19 N

19 N 20 N

20 N

21 Y

21 N 22 N

22 N

23 N

23 N 24 N

24 N

25 Y

25 N 26 N

26 N

27 N

27 N 28 N

28 N

29 N

29 N 30 N

30 N

31 N

31 N 32 N

32 N

33 N

33 N 34 N

34 Y

35 N

35 N 36 N

36 Y

37 N

37 N 38 Y

38 N

39 N

39 N 40 N

40 N

41 Y

41 N 42 N

42 N

43 Y

43 N 44 N

44 N

45 N

45 Y 46 N

46 N

47 N

47 N 48 N

48 N

49 N

49 N 50 N

50 N

51 Y

51 N 52 Y

52 N

53 Y

53 N 54 N

54 N

55 Y

55 N 56 N

56 N


46

Table 14: Concurrent Benzos, Opioids, and Carisoprodol at the Control Pharmacy

Pre-Implementation


# Yes 21

# No 27

# No 27 Cluster

ID Triple Combo

Cluster ID

Triple Combo

1 Y

1 Y 2 N

2 N

3 Y

3 Y 4 Y

4 Y

5 N

5 Y 6 Y

6 Y

7 Y

7 Y 8 Y

8 Y

9 Y

9 N 10 Y

10 N

11 N

11 Y 12 N

12 Y

13 N

13 Y 14 N

14 N

15 N

15 Y 16 Y

16 N

17 Y

17 Y 18 N

18 N

19 N

19 Y 20 N

20 N

21 N

21 N 22 N

22 N

23 N

23 N 24 N

24 N

25 Y

25 Y 26 Y

26 Y

27 Y

27 N 28 Y

28 N

29 N

29 Y 30 Y

30 Y

31 Y

31 N 32 N

32 Y

33 Y

33 Y 34 Y

34 N

35 N

35 N 36 Y

36 N

37 Y

37 N 38 Y

38 N

39 Y

39 Y 40 N

40 N

41 N

41 N 42 Y

42 N

43 Y

43 N 44 N

44 N

45 Y

45 Y 46 N

46 N

47 N

47 N 48 N

48 N

49 N 50 N 51 Y 52 N 53 N


47

Table 15: Results Summary – Concurrent Benzos, Opioids, and Carisoprodol (Intervention)


Total 62 56

Sample Size: 118

Χ2: (p = 0.0045)

Figure 7: % Patients on Concurrent Benzos, Opioids, and Carisoprodol (Intervention)

0%

10%

20%

30%

40%

50%

Pre Post

32%

14%

Percentage

% Patients on Concurrent Benzos, Opioids, and Carisoprodol (Intervention)


48

Table 16: Results Summary – Concurrent Benzos, Opioids, and Carisoprodol (Control)


Total 53 48

Sample Size: 101

Χ2: (p = 0.33)

Figure 8: % Patients on Concurrent Benzos, Opioids, and Carisoprodol (Control)

0%

10%

20%

30%

40%

50%

Pre Post

49% 44%

Percentage

% Patients on Concurrent Benzos, Opioids, and Carisoprodol (Control)


49





pertaining to the pre-implementation or post-implementation time period. The “# Concurrent”

column denotes the number of times there were concurrent multiple prescribers observed. The

“Multiple MD (Y/N)” column denotes whether multiple concurrent providers were observed.

Any value greater than zero in the “# Concurrent” column results in a “Y” in the “Multiple MD

(Y/N” column. Table 19 and Table 20 summarize the “Multiple MD (Y/N)” data for the

intervention and control groups respectively for statistical analysis. Figure 9 and Figure 11

graphically represent Table 19 and Table 20 respectively. Figure 10 is a graphical representation

of the quantity of multiple prescriber frequencies observed in the “# Concurrent” column.


50

Table 17: Multiple Concurrent Prescribers at the Intervention Group

Pre-Implementation

Post-Implementation Average 1.88

Average 1.46

# Yes 25

# Yes 13 # No 25

# No 37

Cluster ID

# Overlap

Multiple MD (Y/N)

Cluster ID

# Overlap

Multiple MD (Y/N)

1 2 Y

1 0 N 2 0 N

2 0 N

3 1 Y

3 1 Y 4 1 Y

4 2 Y

5 1 Y

5 0 N 6 0 N

6 1 Y

7 2 Y

7 2 Y 8 3 Y

8 0 N

9 1 Y

9 0 N 10 1 Y

10 2 Y

11 1 Y

11 0 N 12 1 Y

12 0 N

13 0 N

13 0 N 14 1 Y

14 0 N

15 2 Y

15 0 N 16 2 Y

16 2 Y

17 0 N

17 1 Y 18 0 N

18 1 Y

19 0 N

19 0 N 20 0 N

20 0 N

21 0 N

21 0 N 22 1 Y

22 0 N

23 0 N

23 0 N 24 1 Y

24 0 N

25 0 N

25 0 N 26 1 Y

26 0 N

27 0 N

27 0 N 28 0 N

28 1 Y

29 0 N

29 0 N 30 1 Y

30 0 N

31 3 Y

31 0 N 32 4 Y

32 2 Y

33 0 N

33 2 Y 34 0 N

34 0 N

35 5 Y

35 0 N 36 0 N

36 0 N

37 0 N

37 0 N 38 0 N

38 0 N

39 5 Y

39 0 N 40 4 Y

40 1 Y

41 0 N

41 0 N 42 0 N

42 0 N

43 1 Y

43 0 N 44 0 N

44 0 N

45 0 N

45 0 N 46 0 N

46 0 N

47 0 N

47 0 N 48 1 Y

48 1 Y

49 0 N

49 0 N 50 1 Y

50 0 N


51

Table 18: Multiple Concurrent Prescribers at the Control Group

Pre-Implementation


Average 1.52

# Yes 24

# Yes 21 # No 26

# No 29

Cluster ID

# Concurre

nt Multiple MD

(Y/N)

Cluster ID

# Concurr

ent Multiple MD

(Y/N) 1 0 N

1 0 N

2 0 N

2 1 Y 3 1 Y

3 0 N

4 1 Y

4 1 Y 5 4 Y

5 0 N

6 0 N

6 2 Y 7 1 Y

7 0 N

8 0 N

8 0 N 9 0 N

9 0 N

10 1 Y

10 0 N 11 0 N

11 2 Y

12 0 N

12 2 Y 13 0 N

13 1 Y

14 0 N

14 0 N 15 2 Y

15 0 N

16 0 N

16 0 N 17 0 N

17 1 Y

18 1 Y

18 0 N 19 0 N

19 0 N

20 0 N

20 0 N 21 0 N

21 1 Y

22 0 N

22 0 N 23 0 N

23 0 N

24 1 Y

24 2 Y 25 0 N

25 1 Y

26 0 N

26 0 N 27 0 N

27 1 Y

28 1 Y

28 0 N 29 0 N

29 1 Y

30 1 Y

30 0 N 31 1 Y

31 0 N

32 1 Y

32 1 Y 33 1 Y

33 1 Y

34 1 Y

34 0 N 35 1 Y

35 1 Y

36 0 N

36 2 Y 37 0 N

37 0 N

38 2 Y

38 0 N 39 4 Y

39 0 N

40 2 Y

40 1 Y 41 2 Y

41 5 Y

42 0 N

42 0 N 43 0 N

43 3 Y

44 2 Y

44 0 N 45 1 Y

45 0 N

46 0 N

46 1 Y 47 1 Y

47 0 N

48 4 Y

48 0 N 49 0 N

49 1 Y

50 1 Y

50 0 N


52

Table 19: Results Summary – Multiple Concurrent Prescribers (Intervention)


Total 50 50

Sample Size: 100

Χ2: (p = 0.00069)

Figure 9: % Patients Filling Prescriptions from Multiple Concurrent Providers (Intervention)

Figure 10: Breakdown of Multiple Concurrent Prescribers (Occurrences by Individual)

0% 10% 20% 30% 40% 50% 60%

Pre Post

50%

26% Percentage

% Patients Filling Prescriptions from Multiple Concurrent Providers (Intervention)

0

5

10

15

20

1 2 3 4 5 6

Occurrences

# Multiple Concurrent Prescribers

Pre

Post


53

Table 20: Results Summary – Multiple Concurrent Prescribers (Control)


Total 50 50

Sample Size: 100

Χ2: (p = 0.40)

Figure 11: % Patients Filling Prescriptions from Multiple Concurrent Providers (Control)

0%

10%

20%

30%

40%

50%

60%

Pre Post

48% 42%

Percentage

% Patients Filling Prescriptions from Multiple Concurrent Providers (Control)


54





pertaining to the pre-implementation or post-implementation time period. The “# Concurrent”

column denotes the number of times there were concurrent multiple prescribers observed. The

“Multiple Rx (Y/N)” column denotes whether multiple concurrent providers were observed. Any

value greater than zero in the “# Concurrent” column results in a “Y” in the “Multiple Rx (Y/N”

column. Table 23 and Table 24 summarize the “Multiple Rx (Y/N)” data for the intervention and

control groups respectively for statistical analysis. Figure 12 and Figure 14 graphically represent

Table 23 and Table 24 respectively. Figure 13 is a graphical representation of the quantity of

multiple prescriber frequencies observed in the “# Concurrent” column.


55

Table 21: Multiple Concurrent Pharmacy Filling at the Intervention Group

Pre-Implementation


Average 2.00

# Yes 16

# Yes 6 # No 34

# No 44

Cluster ID

# Concurrent

Multiple Rx

(Y/N)

Cluster ID

# Concurrent

Multiple Rx

(Y/N) 1 3 Y

1 0 N

2 1 Y

2 0 N 3 2 Y

3 1 Y

4 1 Y

4 0 N 5 0 N

5 0 N

6 0 N

6 1 Y 7 2 Y

7 1 Y

8 5 Y

8 0 N 9 0 N

9 6 Y

10 0 N

10 0 N 11 2 Y

11 0 N

12 3 Y

12 0 N 13 0 N

13 0 N

14 0 N

14 0 N 15 5 Y

15 0 N

16 0 N

16 0 N 17 1 Y

17 0 N

18 0 N

18 0 N 19 0 N

19 1 Y

20 0 N

20 0 N 21 0 N

21 0 N

22 0 N

22 0 N 23 0 N

23 0 N

24 0 N

24 0 N 25 0 N

25 0 N

26 2 Y

26 0 N 27 0 N

27 0 N

28 3 Y

28 0 N 29 0 N

29 0 N

30 0 N

30 0 N 31 0 N

31 2 Y

32 4 Y

32 0 N 33 0 N

33 0 N

34 1 Y

34 0 N 35 0 N

35 0 N

36 0 N

36 0 N 37 0 N

37 0 N

38 1 Y

38 0 N 39 0 N

39 0 N

40 0 N

40 0 N 41 0 N

41 0 N

42 4 Y

42 0 N 43 0 N

43 0 N

44 0 N

44 0 N 45 0 N

45 0 N

46 0 N

46 0 N 47 0 N

47 0 N

48 0 N

48 0 N 49 0 N

49 0 N

50 0 N

50 0 N


56

Table 22: Multiple Concurrent Pharmacy Filling at the Control Group

Pre-Implementation


Average 1.75

# Yes 14

# Yes 13 # No 36

# No 37

Cluster ID

# Concurrent

Multiple Rx

(Y/N)

Cluster ID

# Concurrent

Multiple Rx

(Y/N) 1 1 Y

1 0 N

2 0 N

2 0 N 3 1 Y

3 0 N

4 0 N

4 0 N 5 0 N

5 1 Y

6 0 N

6 0 N 7 3 Y

7 0 N

8 0 N

8 1 Y 9 0 N

9 0 N

10 0 N

10 0 N 11 0 N

11 0 N

12 0 N

12 0 N 13 1 Y

13 1 Y

14 0 N

14 2 Y 15 0 N

15 0 N

16 0 N

16 0 N 17 2 Y

17 0 N

18 0 N

18 2 Y 19 0 N

19 0 N

20 0 N

20 0 N 21 0 N

21 0 N

22 1 Y

22 0 N 23 0 N

23 0 N

24 0 N

24 1 Y 25 2 Y

25 0 N

26 0 N

26 0 N 27 0 N

27 2 Y

28 1 Y

28 0 N 29 0 N

29 0 N

30 0 N

30 0 N 31 0 N

31 0 N

32 0 N

32 6 Y 33 5 Y

33 0 N

34 1 Y

34 0 N 35 2 Y

35 0 N

36 0 N

36 1 Y 37 0 N

37 0 N

38 0 N

38 0 N 39 0 N

39 0 N

40 0 N

40 1 Y 41 1 Y

41 0 N

42 0 N

42 0 N 43 1 Y

43 0 N

44 0 N

44 1 Y 45 0 N

45 1 Y

46 0 N

46 0 N 47 0 N

47 1 Y

48 0 N

48 0 N 49 0 N

49 0 N

50 2 Y

50 0 N


57

Table 23: Results Summary – Multiple Concurrent Pharmacies (Intervention)


Total 50 50

Sample Size: 100

Χ2: (p = 0.0024)

Figure 12: % Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Intervention)

Figure 13: Breakdown of Multiple Concurrent Pharmacies (Occurrences by Individual)

0% 5% 10% 15% 20% 25% 30% 35%

Pre Post

32%

12% Percentage

% Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Intervention)

0

1

2

3

4

5

6

1 2 3 4 5 6

Occurrences

# Concurrent Multiple Pharmacies

Pre

Post


58

Table 24: Results Summary – Multiple Concurrent Pharmacies (Control)


Total 50 50

Sample Size: 100

Χ2: (p = 0.75)

Figure 14: % Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Control)

0%

10%

20%

30%

40%

Pre Post

28% 26%

Percentage

% Patients Filling Prescriptions from Multiple Concurrent Pharmacies (Control)


59

CHAPTER 5 - CONCLUSIONS

Summary of Findings

When interpreting the following results, keep in mind that findings span a full year pre-

and post- PDMP use policy implementation. Some findings indicating possible aberrant behavior

may seem to be larger than expected unless interpreted in this context.

Maximum Morphine Daily Dose

In the intervention group, the researchers found a statistically significant difference (p =

0.022) between pre- and post-implementation of the PDMP use policy for the proportion of

patients >120 mg maximum morphine daily dose (Table 3). Prior to implementing the PDMP use

policy, the intervention pharmacy had 42% of the sample population with maximum morphine

daily equivalent doses >120 mg (Figure 1). After implementation of the PDMP use policy, the

intervention pharmacy had 26% of the sample population with maximum morphine daily

equivalent doses >120 mg (Figure 1). This is statistically significant decrease of 16%. We

attribute this statistically significant reduction in the percentage maximum morphine daily

equivalent doses >120 mg to the implementation of the PDMP use policy.

In the intervention group, the researchers found equal variance (F = 0.53) between pre-

and post-implementation of the PDMP use policy, indicating that the variance is similar and not

due to the intervention (Table 1). We did not find a statistically significant difference (p = 0.15)

between pre-and post-implementation average maximum morphine equivalent daily doses for the

sample population; however there was a reduction from 116 mg to 101 mg (Table 1). Although,

not statistically significant, we attribute this reduction in the average maximum morphine daily

equivalent doses to the implementation of the PDMP use policy and expect that with a larger


60

sample size statistical significance would have been seen due to the magnitude of reduction and

p value compared to the control group.

In the control group, the researchers did not find a statistically significant difference (p =


patients with >120 mg maximum morphine daily dose (Table 4). Prior to implementing the

PDMP use policy, 53% of the sample population at the intervention pharmacy had a maximum

morphine daily equivalent doses >120 mg (Figure 2). After implementation of the PDMP use

policy, the intervention pharmacy had 49% of the sample population with maximum morphine

daily equivalent doses >120 mg (Figure 2). This is not a statistically significant decrease of 4%.

The researchers had not expected to find any statistically significant findings in the control

group.

In the control group, the researchers found equal variance (F = 0.72) between pre- and

post-implementation of the PDMP use policy, indicating that the variance is similar and not due

to the intervention (Table 2). We did not find a statistically significant difference (p = 0.41)

between pre-and post-implementation average maximum morphine equivalent daily doses for the

sample population (Table 2). We would not have expected to find any statistically significant

findings in the control group.

In summary, implementation of the PDMP use policy resulted in a statistically significant

decrease in the proportion of patients with a maximum daily morphine equivalent dose >120 mg.

The researchers attribute this statistically significant reduction in the percentage maximum

morphine daily equivalent doses >120 mg to the implementation of the PDMP use policy. There

was a reduction in the average sample population maximum morphine equivalent dose between

pre- and post-implementation of the PDMP use policy, however, it is not statistically significant.


61

Although, not statistically significant, we attribute this reduction in the average maximum

morphine daily equivalent doses to the implementation of the PDMP use policy and expect that

with a larger sample size statistical significance would have been seen due to the magnitude of

reduction and p value compared to the control group. There are no statistically significant

findings to report in the control group as expected.




patients with >40 mg methadone daily dose (Table 7). Prior to implementing the PDMP use

policy, the intervention pharmacy had 44% of the sample population with methadone daily

equivalent doses >40 mg (Figure 3). After implementation of the PDMP use policy, the

intervention pharmacy had 11% of the sample population with methadone daily equivalent doses

>40 mg (Figure 3). This is a statistically significant decrease of 33%. The researchers attribute

this statistically significant reduction in the percentage maximum methadone daily doses >40 mg

to the implementation of the PDMP use policy.

In the intervention group, the researchers found unequal variance (F = 0.0016) between

pre- and post-implementation of the PDMP use policy, indicating that the variance is not similar

and is due to the intervention (Table 5). There was a statistically significant difference (p =

0.0077) between pre- and post-implementation average methadone daily doses for the sample

population, with a reduction from 45 mg to 19 mg (Table 5). We attribute this statistically

significant reduction in the average maximum methadone daily doses >40 mg to the

implementation of the PDMP use policy.


62



patients with >40 mg methadone daily dose (Table 8). Prior to implementing the PDMP use

policy, the control pharmacy had 66% of the sample population with methadone daily equivalent

doses >40 mg (Figure 4). After implementation of the PDMP use policy, the control pharmacy

had 63% of the sample population with methadone daily doses >40 mg (Figure 4). This is not a

statistically significant decrease of 3%. We would not have expected to find any statistically

significant findings in the control group.

In the control group, the researchers found equal variance (F = 0.92) between pre- and

post-implementation of the PDMP use policy, indicating that the variance is similar and not due

to the intervention (Table 6). We did not find a statistically significant difference (p = 0.48)

between pre-and post-implementation average methadone daily doses for the sample population

(Table 6). The researchers did not expect to find any statistically significant findings in the

control group.


decrease in the proportion of patients with a maximum daily methadone dose of >40 mg. The

researchers attribute this statistically significant reduction in the percentage maximum

methadone daily doses >40 mg to the implementation of the PDMP use policy. There is a

statistically significant reduction in the average sample population maximum methadone dose

between pre- and post-implementation of the PDMP use policy. We attribute this statistically

significant reduction in the average maximum methadone daily doses >40 mg to the

implementation of the PDMP use policy. There are no statistically significant findings to report

in the control group as expected.


63


In the intervention group, the researchers did not find a statistically significant difference

(p = 0.39) between pre- and post-implementation of the PDMP use policy for concurrent

benzodiazepine and opioid use (Table 11). Prior to implementing the PDMP use policy, the

intervention pharmacy had 44% of the sample population with concurrent benzodiazepine and

opioid use (Figure 5). After implementation of the PDMP use policy, the intervention pharmacy

had 38% of the sample population with concurrent benzodiazepines and opioids (Figure 5). This

is a non-statistically significant decrease of 6%. Due to the intervention pharmacy screening for

benzodiazepines, opioids, and carisoprodol (muscle relaxants) and not specifically for

benzodiazepines and opioids, we did not expect to find a statistically significant decrease in

concurrent benzodiazepines and opioids alone, as these results confirm.


0.78) between pre- and post-implementation of the PDMP use policy for concurrent

benzodiazepines and opioids (Table 12). Prior to implementing the PDMP use policy, the

intervention pharmacy had 44% of the sample population with concurrent benzodiazepines and

opioids (Figure 6). After implementation of the PDMP use policy, the intervention pharmacy had

42% of the sample population with concurrent benzodiazepines and opioids (Figure 6). This is a

non-statistically significant decrease of 2%. We would not have expected to find any statistically

significant findings in the control group.

In summary, there are no statistically significant findings to report for either the

intervention or control groups for this analysis.


64

Concurrent Benzodiazepines, Opioids & Carisoprodol

In the intervention group, we found a statistically significant difference (p = 0.0045)

between pre- and post-implementation of the PDMP use policy for concurrent benzodiazepines,

opioids and carisoprodol (Table 15). Prior to implementing the PDMP use policy, the

intervention pharmacy had 32% of the sample population with concurrent benzodiazepines,

opioids, and carisoprodol (Figure 7). After implementation of the PDMP use policy, the

intervention pharmacy had 14% of the sample population with concurrent benzodiazepines,

opioids, and carisoprodol (Figure 7). This is a statistically significant decrease of 18%. The

researchers attribute this statistically significant reduction in the concurrent benzodiazepines,

opioids, and carisoprodol to the implementation of the PDMP use policy.


0.33) between pre and post implementation of the PDMP use policy for concurrent

benzodiazepines, opioids and carisoprodol (Table 16). Prior to implementing the PDMP use

policy, the control pharmacy had 49% of the sample population with concurrent

benzodiazepines, opioids, and carisoprodol (Figure 8). After implementation of the PDMP use

policy, the control pharmacy had 44% of the sample population with concurrent

benzodiazepines, opioids, and carisoprodol (Figure 8). This is a statistically non-significant

decrease of 5%. The researchers did not expect to find any statistically significant findings in the

control group.

In summary, the researchers found a statistically significant reduction in the proportion of

patients taking concurrent benzodiazepines, opioids, and carisoprodol between pre- and post-

implementation of the PDMP use policy in the intervention group. We attribute this statistically

significant reduction in the concurrent benzodiazepines, opioids, and carisoprodol to the


65

implementation of the PDMP use policy. There are no statistically significant findings to report

for the control group as expected.




prescribers (Table 19). Prior to implementing the PDMP use policy, the intervention pharmacy

had 50% of the sample population with concurrent prescribers (Figure 9). After implementation

of the PDMP use policy, the intervention pharmacy had 26% of the sample population with

concurrent prescribers (Figure 9). This is a statistically significant decrease of 24%. The

researchers attribute this statistically significant reduction in the multiple concurrent prescribers

to the implementation of the PDMP use policy.


0.40) between pre- and post-implementation of the PDMP use policy for concurrent prescribers

(Table 20). Prior to implementing the PDMP use policy, the control pharmacy had 48% of the

sample population with concurrent prescribers (Figure 11). After implementation of the PDMP

use policy, the control pharmacy had 42% of the sample population with concurrent prescribers

(Figure 11). This is a non-statistically significant decrease of 6%. The researchers did not expect

to find any statistically significant findings in the control group.


patients filling prescriptions from multiple concurrent providers between pre and post

implementation of the PDMP use policy in the intervention group. We attribute this statistically

significant reduction in multiple concurrent prescribers to the implementation of the PDMP use

policy. There are no statistically significant findings to report for the control group as expected.


66

The researchers found that the frequency among patients who did utilize multiple concurrent

prescribers mainly did so one time with diminishing percentages of patients who did so multiple

times.




pharmacies (Table 23). Prior to implementing the PDMP use policy, the intervention pharmacy

had 32% of the sample population with concurrent pharmacies (Figure 12). After implementation

of the PDMP use policy, the intervention pharmacy had 12% of the sample population with

concurrent pharmacies (Figure 12). This is a statistically significant decrease of 20%. The

researchers attribute this statistically significant reduction in multiple concurrent pharmacies to

the implementation of the PDMP use policy.


0.75) between pre- and post-implementation of the PDMP use policy for concurrent pharmacies

(Table 24). Prior to implementing the PDMP use policy, the control pharmacy had 28% of the

sample population with concurrent pharmacies (Figure 14). After implementation of the PDMP

use policy, the control pharmacy had 26% of the sample population with concurrent prescribers

(Figure 14). This is a non-statistically significant decrease of 2%. The researchers did not expect

to find any statistically significant findings in the control group.


patients filling prescriptions at multiple concurrent pharmacies between pre- and post-

implementation of the PDMP use policy in the intervention group. The researchers attribute this

statistically significant reduction in the multiple concurrent pharmacies to the implementation of


67

the PDMP use policy. There are no statistically significant findings to report for the control

group as expected. The researchers found that the frequency of patients who utilized multiple

concurrent pharmacies mainly did so one time with diminishing percentages of patients who did

so multiple times.

Actual Patient Case Examples

The intervention pharmacy has kept a log of interventions that have been made since

implementing the PDMP use policy. The following are summaries of a sample of those

interventions, included to help understand the real life impact of PDMP utilization:

1. >120 mg Maximum Morphine Equivalent Daily Dose – The patient presented with a

prescription for a total daily dose of oxycodone 200 mg (300 mg morphine equivalent

daily dose). The PDMP revealed that the patient filled at a different pharmacy. The

patient was referred back to that pharmacy.

2. >40 mg Daily Methadone Dose – A prescription was presented for methadone 10 mg

tablets, 4 tablets by mouth three times daily (120 mg daily). Per the PDMP database, the

patient recently had a 30 day supply filled. Prescription was not filled.

3. Concurrent Benzodiazepines, Opioids, and Carisoprodol – A prescription was

presented for oxycodone. When the patient was looked up in the PDMP, they were found

to have prescriptions for ambien, xanax (benzodiazepine) and carisoprodol, from one

prescriber and received oxycodone from another prescriber.

4. Multiple Concurrent Prescribers – The PDMP database showed multiple prescribers of

an opioid (Percocet). The pharmacist called each prescriber to ensure that they were

aware of multiple prescribers. They were not aware and the prescription was cancelled,

not returned to the patient, and filed with documentation.


68

5. Multiple Concurrent Pharmacies – A prescription was presented for 30 Percocet

tablets. The PDMP revealed that the same prescription was filled the day before at

another pharmacy. After calling the other pharmacy, it was discovered that the

prescription filled the day prior stated: “must last 10 days.” The prescribing physician’s

office was already closed, so the pharmacist could not obtain approval to fill. When

spoken to, the patient further admitted that they were in a pain contract and supposed to

use only one pharmacy. The prescription was returned to the patient and they were told

that they would have to use the pharmacy on the pain contract.

6. Early Refills – The patient was dismissed from future filling at the pharmacy for seeking

a refill of a benzodiazepine (lorazepam) early and becoming belligerent and threatening.

Recommendations for the sponsoring organization

1. Continue screening all patients filling prescriptions for opioids per a PDMP use policy.

2. Specifically list the maximum daily morphine equivalent dose >120 mg in screening

criteria.

3. Specifically list a maximum methadone daily dose >40 mg in screening criteria.

4. Continue to screen for concurrent benzodiazepines, opioids, and carisoprodol (or other

muscle relaxants) in the pharmacy software.

5. Begin screening for concurrent benzodiazepines and opioids.

6. Specifically list multiple concurrent prescribers in screening criteria.

7. Specifically list multiple concurrent pharmacies in screening criteria.

Recommendations for Regulatory Bodies

Based on the results of this study and due to the risks of aberrant behaviors as

summarized in the literature in Chapter 2, which indicate a public safety concern, abuse,


69

addiction, or increased risk of overdose and mortality, we recommend requiring pharmacy

PDMP use policies for all pharmacies that include the following criteria: (1) maximum daily

morphine equivalent dose >120 mg, (2) maximum daily methadone dose >40 mg, (3) concurrent

benzodiazepines, opioids, and carisoprodol, (4) filling of prescriptions from multiple concurrent

providers, and (5) filling of prescriptions at multiple concurrent pharmacies.

General approaches for future research

1. Utilize the findings of this research to develop a standardized screening tool, which

specifically incorporates the criteria in this study for use across organizations.

2. After implementing the standardized screening tool, repeat the study with larger sample

sizes in each category. Possibly study the impact statewide after statewide

implementation of a PDMP use policy requirement.

3. Include the multiple concurrent long-acting and short-acting as well as early refill

research into future studies now that the PDMP has days supply data.

4. Study the outcomes of contacting prescribers when aberrant behavior criteria are

identified.

Limitations of the study

The sex of the patient, days supply, and refill information were not collected in 2013 as

they were not included in the data variables listed in statute. During the 2013 legislative session,

the Oregon PDMP statute was amended to allow days supply, refill information, and patient sex

to be collected beginning January 1, 2014. Since pharmacies had to reconfigure their systems to

report these new variables and with the ramp-up time needed to make these changes, these new

variables may not have been reported by all pharmacies beginning January 1, 2014. As such,

there is a higher probability that the quality of the data related to these variables may not be as


70

high early in 2014. In addition, this has placed limitations on data collection and analysis for

days supply and refill information as part of data collection and analyses for early refills from

those filling opioid prescriptions at a retail pharmacy.

We do not know the outcomes of calling prescribers during the pre- and post-

implementation periods, where prescribers may have given permission to fill the prescription, in

which case it is included in the data set. When a prescriber withdrew the prescription it would

not have been filled, and therefore is not included in the data set.

Although the sample size was adequate to detect statistical significance, a larger sample

size is preferable. Due to data analysis and time limitations, as well as the number of outcomes

evaluated in this study, sample sizes totaling around 200 patients (intervention plus control) were

used for most interventions.

Due to the lack of days supply information we utilized intervals between prescription

filling as well as standard prescribing patterns to determine the maximum morphine daily dosing

and methadone daily dosing. The same methodology was used in the intervention and control

groups; however, days supply information would have aided the analysis.

Due to limitations imposed by Oregon law, disclosure of the prescriber DEA number was

not permitted. Therefore, direct identification of cancer patients by screening out prescriptions

from oncologists was not possible. The same methodology was used in intervention and control

groups.

The researchers relied on liquid dosing forms and additional prescriptions common in

hospice patients to screen out this patient population. The same methodology was used in

intervention and control groups.


71

Although statistically significant differences were found when looking up patients in the

PDMP database, some of them could be discovered on a more limited basis from the pharmacy’s

prescription filling software. Use of the PDMP, however, allows for broader detection by

including the filling history at other pharmacies. Analysis of maximum doses and concurrent

benzodiazepines, opioids, and carisoprodol utilized prescriptions filled at just the intervention

and control pharmacies because those pharmacies would not have the ability to make an

intervention at other pharmacies. With statistically significant differences seen, even with this

limitation, we expect even greater differences if all pharmacies implemented a PDMP use policy.

Conclusions


decrease in the proportion of patients with a maximum daily morphine equivalent dose >120 mg,

maximum daily methadone dose >40 mg, concurrent benzodiazepines, opioids, and carisoprodol,

filling of prescriptions from multiple concurrent providers, and filling of prescriptions at multiple

concurrent pharmacies. The researchers recommend including these criteria in PDMP use

policies used by pharmacists accessing the PDMD to identify patient aberrant behaviors, which

could indicate abuse, addiction, or increased risk of overdose and mortality.


72

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APPENDIX A

Sponsor Support Letter

September 2, 2014 To Whom It May Concern: I am writing in support of the research proposed by Matthew White and Micaiah Fifer titled “Evaluation of Pharmacist Screening Criteria for Intervention in Opioid Prescription Dispensing to Enhance Patient Safety and Reduce Aberrant Behavior”. As a pharmacist with over 20 years of practice experience in Southern Oregon, I have continuously worried about the inappropriate use of opiates and the impact on our communities. As stated in the rational for this research proposal “unintentional prescription drug-related hospitalizations and deaths have risen steadily since 2000 indicating that there is a clear safety issue that corresponds to opioid abuse.” It is clear that my worries are not unwarranted. The Oregon Prescription Drug Monitoring Program (PDMP) has finally given the medical community a resource to identify potentially aberrant behavior. However, each pharmacy and pharmacist is struggling to understand this new data in relationship to the safety of our patients and our communities. With each pharmacist interpreting this data using different processes, patient are treated inconsistently across pharmacies and doctors are receiving mixed messages regarding their patients. The research proposed by Mr. White and Mr. Fifer will provide pharmacists with standard screening criteria and allow for consistent use of the data provided by the Oregon PDMP. I feel confident that consistent evaluation of opiate use across all pharmacies would help the medical community provide safe pain management for our patients while keeping our communities a safer place to live. Sincerely, [Sponsor Omitted]


81

APPENDIX B

Public Health Division / Multnomah County Health Department IRQ


82


83


84


85


86


87


88


89


90


91


92


93


94


95


96


97


98

APPENDIX C


99

APPENDIX D

Public Health IRB Approval Letter


100

APPENDIX E

Southern Oregon IRB Approval Letter


101

APPENDIX F

Pacific University IRB Exempt Application


102


103


104


105


106


107

APPENDIX G

Pacific University IRB De-Identified HIPAA Form


108


109


110

APPENDIX H

Pacific University IRB Approval Letter


111

APPENDIX I

Timeline

October 2013

• Formulation of partnership / PDMP chosen as subject

Nov-Jan 2013

• PDMP as screening tool discussed and chosen as topic

February-March 2013

• Chapter 1 drafted

March 2014

• Chapter 1 submitted For review to Pacific

• Research committee team personnel selected and formally joined

• IRB project account created for Pacific IRB

• Oregon Board of Pharmacy contacted to discuss committee membership and future

surveying of members

April 2014

• Literature review conducted

• Capstone committee formed

• Ky Fifer signed affiliation / CHP agreement to intern at [Organization Omitted]

May 2014

• Literature review submitted to Pacific for review

• Capstone committee form submitted to Pacific for review


112

• Methodology submitted to Pacific for review

June 2014

• Initial PDMP Data Use Agreement submitted to OHA

• Revised Chapter 1 submitted to Pacific

• Revised literature review submitted to Pacific

• Draft of chapters 1-3 submitted to Carole Londeree for APA format review

July 2014

• Revised methodology submitted to Pacific

• Project proposal presentation at Pacific to faculty and 2nd year students

• Letter of support requested from OHA for Pacific IRB

• OHA notifies research team that data must be de-identified on OHA site

• Sponsoring pharmacy IRB gives oversight to Pacific IRB

August 2014

• Signed initial draft of Data use Agreement sent to OHA

• OHA reviews and outlines required updates for Data Use Agreement

• Chapters 1-3 reviewed and approved by Dr. Bobby Nijjar from Pacific

University

• Updated Data Use Agreement is sent to Pacific personnel and is signed

• Updated information uploaded to Pacific IRB processing website

• Initial submission to Pacific IRB submitted by Dr. Bobby Nijjar

• OHA IRB submitted via electronic submission process

• Further Data Use Agreement addendums are implemented by OHA personnel


113

September 2014

• [Sponsor Omitted] letter of support from [sponsor Omitted] provided for IRB submission

• Sponsor letter of support uploaded to Pacific IRB

October 2014

• Data Use agreement signed by Pacific University

• CV and resumes submitted to Oregon Health Authority upon request

• New Oregon Health Authority Data Use Agreement addendums added via email

correspondence from Oregon Health Authority

• On-site meeting with Oregon Health Authority personnel to discuss Data Use Agreement

updates

• Pacific University IRB told to hold submission pending updated Oregon Health Authority

paperwork

• Oregon Health Authority Data Use Agreement, Initial Review Questionnaire, and HIPAA

forms drafted / submitted to Oregon Health Authority

• Asante grants IRB exemption

• Updated Data Use Agreement signed by Pacific research team

• CV’s, support letter, Data Use Agreement, Initial Review Questionnaire, HIPAA De-

identified Form, Southern Oregon IRB Exemption Letter submitted to Oregon Health

Authority

November 2014

• Additional correspondence with Oregon Health Authority for Data Use Agreement

• All pertinent documents submitted to Oregon Health Authority IRB

• OHA IRB determines project does not utilize human subjects and is formally approved


114

December 2014

• Project formally submitted for review for Pacific IRB

January 2015

• Project formally approved by Pacific IRB

February 2015

• De-identified data is picked up from OHA by researchers

• Data analysis begins

March 2015

• Data analysis completion

• Perform statistical analysis

• Draft chapter 4 and 5

• Submit final draft for review

April-May 2015

• Final approval from Pacific University

• Binding

• Presentations

June-July 2015

• Presentations