Background: Systematic implementation of guidelines for opioid therapy management in chronic non-cancer pain can reduce opioid-related harms. However, implementation of guideline-recommended practices in routine care is subpar. The goal of this quality improvement (QI) project is to assess whether a clinic-tailored QI intervention improves the implementation of a health system-wide, guideline-driven policy on opioid prescribing in primary care. This manuscript describes the protocol for this QI project. Methods: A health system with 28 primary care clinics caring for approximately 294,000 primary care patients developed and implemented a guideline-driven policy on long-term opioid therapy in adults with opioid-treated chronic non-cancer pain (estimated N = 3980). The policy provided multiple recommendations, including the universal use of treatment agreements, urine drug testing, depression and opioid misuse risk screening, and standardized documentation of the chronic pain diagnosis and treatment plan. The project team drew upon existing guidelines, feedback from end-users, experts and health system leadership to develop a robust QI intervention, targeting clinic- level implementation of policy-directed practices. The resulting multi-pronged QI intervention included clinic-wide and individual clinician-level educational interventions. The QI intervention will augment the health system’s “routine rollout” method, consisting of a single educational presentation to clinicians in group settings and a separate presentation for staff. A stepped-wedge design will enable 9 primary care clinics to receive the intervention and assessment of within-clinic and between-clinic changes in adherence to the policy items measured by clinic-level electronic health record-based measures and process measures of the experience with the intervention. Discussion: Developing methods for a health system-tailored QI intervention required a multi-step process to incorporate end-user feedback and account for the needs of targeted clinic team members. Delivery of such tailored QI interventions has the potential to enhance uptake of opioid therapy management policies in primary care. Results from this study are anticipated to elucidate the relative value of such QI activities. Keywords: Opioid analgesics, Chronic pain, Quality improvement, Healthcare systems, Healthcare quality, Access and evaluation * Correspondence: Aleksandra.Zgierska@fammed.wisc.edu Department of Family Medicine and Community Health, Wisconsin Research and Education Network, School of Medicine and Public Health, University of Wisconsin-Madison, 1100 Delaplaine Court, Madison, WI 53715, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Zgierska et al. BMC Health Services Research (2018) 18:415 Page 2 of 8 Background guideline-recommended opioid prescribing practices in Chronic non-cancer pain (“chronic pain”)iscommon, primary care, compared to the routine rollout efforts of affecting over 100 million Americans . It is often refractory the health system. Future reports will describe the out- to existing treatments, and many patients are prescribed opi- comes of this QI project. oids to reduce pain and disability. However, long-term opi- oids are controversial for chronic pain and have been linked Methods/design to dose-dependent harm, including addiction and overdose Project aim death [2, 3]. Prescribed opioids serve as the main drug supply The studied health system had planned to implement a pol- for approximately 85% of those who misuse opioids . In icy for the management of long-term opioid therapy in the US, opioid-related overdose deaths have dramatically in- adults with chronic non-cancer pain (“opioid policy”)inits creased, making this a national public health crisis. primary care family medicine (FM) and general internal Systematic implementation of guidelines for opioid medicine (GIM) clinics. The project team, comprised of therapy has the potential to reduce inappropriate pre- physicians, researchers and educators, an electronic health scribing and its harmful effects [5–8]. Primary care clini- record (EHR) database analyst, and a biostatistician, cians account for about half of opioid prescribing [9, 10], hypothesized the health system’s planned routine rollout thus primary care clinical teams are a logical target for approach to the implementation of opioid management quality improvement (QI) initiatives focused on improv- policy may be suboptimal due to the complexity of opioid ing opioid prescribing practices. A modest reduction in prescribing guidelines, the variety of team cultures and opioid prescribing rates was noted in a single academic practices within the system, and the expected discomfort of medical system after a month-long QI effort that focused clinicians in relation to the topic and complexity of the tar- on the dissemination of information on opioid prescrib- get patient population [14–19]. The project team proposed ing guidelines at meetings and via individual in-person and developed a multi-pronged QI intervention aimed to or email communication with primary care clinicians augment the health system’s routine rollout implementation . A QI project at two rural emergency departments in efforts, and designed an outcome evaluation plan to rigor- Maine aimed at reducing prescribing of controlled sub- ously test the intervention effectiveness. Institutional Re- stances for painful dental conditions led to an absolute view Board review was not required because, in accordance reduction in opioid prescribing by 17% (. A with federal regulations, this was deemed a QI project not multi-pronged, statewide effort in Utah, consisting of constituting research, as defined under 45 CFR 46.102(d). formal presentations and ongoing QI efforts with pri- mary care physicians, led to a 14% decrease in the state’s Settings/target population opioid-related deaths . Target population Dissemination of evidence-based recommendations into In January 2016, the health system provided care for 293,927 routine practice is critical for system-wide QI. Historically, primary care patients, including 204,680 adults, defined as however, adoption of guidelines has been slow and challen- 18 years old or older, across its 28 primary care clinics (18 ging , and research on effective methods for dissemin- FM; 10 GIM). Among adult patients, 3980 (1.9%) were esti- ation and implementation of guidelines is limited [12, 13]. mated to be treated with opioids for at least 3 months for In addition, guidelines on opioid therapy management are chronic pain (“target population”). Among target population complex and based largely on expert consensus with lim- patients (59% women; mean age: 53.3 ± 14.2 years), 38.8% ited research evidence, factors that likely affect the adoption were prescribed opioids at ≥50, and 26.0% at ≥90 morphine of these guidelines in routine care [10, 14–16]. milligram equivalent (MME) per day. According to the Cen- The project team therefore decided to develop, execute, ters for Disease Control and Prevention guidelines, the 50 and evaluate the impact of a tailored, multi-pronged QI MME/day threshold is a recommended maximum dose for intervention aimed at increasing primary care clinicians’ most opioids, with doses at or above 90 MME/day recom- adherence to guideline-recommended practices for opioid mended to be avoided . In the target population, 39% therapy in chronic pain. Coincidentally, the local health were co-prescribed benzodiazepines and opioids (based on system was initiating a guideline-driven opioid manage- the “active medication” list), 64.7% had a documented treat- ment policy for this patient population. The routine roll- ment agreement, 32.8% completed urine drug testing, and out efforts by the health system to implement this policy 21.9% completed a depression screening using a validated served as a platform on which to build and test the effects screening tool in the prior 12 months. of a tailored, enhanced QI intervention, targeting safe and competent opioid prescribing. This report describes the Health System’s opioid policy and implementation efforts design, development, and methods for execution of the QI (“routine rollout”) intervention. The aim of this project is to test if an en- The health system’s opioid policy was finalized in June of hanced QI intervention can improve implementation of 2015 and based on existing guidelines [14, 15]. The policy Zgierska et al. BMC Health Services Research (2018) 18:415 Page 3 of 8 was developed by a multidisciplinary panel of clinicians, QI intervention pharmacists, scientists, and policy-implementation spe- The QI intervention was developed over a 12 month period cialists. It was designed to target adult primary care pa- (January–December 2015) and designed to augment the tients who were treated with long-term opioids for “routine rollout” implementation efforts of the health system. chronic non-cancer pain. The policy excluded those under The intervention was informed by the following: 1) the hospice care, with life expectancy shorter than 6 months, goals of the health system’s new opioid management policy; or cancer pain. The health system’s Information Technol- 2) feedback from the health system’s leadership, the policy ogy team developed the interface and tools in the EHR to implementation team and participants at the pilot clinics; be compatible with, and facilitate the implementation of, and 3) the project team’s combined expertise in primary the opioid policy by primary care clinical staff. The policy care, addiction medicine, opioid therapy management, im- recommendations included the initiation and regular up- plementation science, health services research, including date of treatment agreements; urine drug testing; screen- knowledge of the available EHR-based outcome measures ing for depression and the risk of opioid misuse; checking and clinical “charting” tools, medical education design and the state’s PrescriptionDrugMonitoring Program implementation, practice facilitation and statistical analysis. (PDMP) database; and documentation in the EHR of the The intervention educational content was updated as chronic pain diagnosis, clinical progress, and treatment needed to reflect changes in relevant guidelines or law. plan (Table 1). The intervention consists of several components (Table 2): The health system developed a policy implementation training program for its primary care prescribers and 1) Academic Detailing At the beginning of each clinical support staff that was pilot-tested from September intervention, two physician study members (AZ or – November 2015 in one FM and two GIM community DH) deliver an on-site 1-h presentation to clinic staff clinics. Based on feedback from the pilot sites, the health about the study goals; summary of the health system’s system refined the implementation methods and initiated opioid policy and dangers of co-prescribing opioids the system-wide implementation effort (“routine rollout”) and benzodiazepines; and an overview of the QI in February 2016. Project team members met four times intervention and available educational credits. The with the health system leadership; observed the health presentation includes 32 slides to be delivered over system-led pilot implementation efforts in 3 clinics (3 approximately 30 min, with the remaining time in-person educational training sessions on policy imple- designated for discussion with the clinic staff. mentation; 3 teleconference debriefing sessions on the 2) Online Educational Modules The health system’s pilot-clinic experiences); and attended all of the opioid policy, feedback from the pilot clinics, and system-wide policy rollout activities. The “routine” the expertise of team members and invited external system-wide rollout consisted of: 1) a single, in-person experts shaped the development of two online 1-h introductory meeting for groups of clinicians; 2) a 1-h educational modules. Both modules incorporate online training module for staff to be completed under the evidence-based, system-specific, process-related in- clinic managers’ supervision; and 3) two follow-up tele- formation to make the knowledge gained relevant conference sessions led by the health system’s clinical to “real-life” primary care in the health system’s knowledge implementation team to address any questions clinics. Each module consists of 20–21 questions, or comments from prescribers and other clinical staff. delivered via email (1–2 questions every 1–2 days), with multiple-choice answers and a brief rationale Design for correct and incorrect answers. The “Responsible Overall design Opioid Prescribing” module emphasizes real-life Based on a sample size calculation, described in the Statis- implementation of the opioid management policy in tical Analysis section, the project team proposed to enroll 9 the context of the health system-specific clinical of the 28 health system’s primary care clinics into a settings. The “Shared Decision Making” module stepped-wedge 18-month trial. The clinics with the highest includes clinical cases linking information about rates of opioid prescribing for adult patients with chronic shared decision-making principles to the care for pain will be approached first. Each enrolled clinic will start patients with opioid-treated chronic pain. as a control site; then, in waves of 3, clinics will sequentially 3) Practice Facilitation (PF) PF is a structured receive the intervention until all become intervention sites. approach to assist participating clinics with Use of a stepped-wedge design, coupled with outcome site-specific interventions focused on promoting measures assessed via EHR-based data, will allow an effi- workflow change . Trained practice facilitators cient, rigorous and controlled evaluation of the effective- work with the clinic staff to identify each clinic’s ness of the proposed intervention that, if proven successful, incremental goals for change, developing a plan to can be rapidly disseminated across an entire health system. accomplish the selected change, and evaluating Zgierska et al. BMC Health Services Research (2018) 18:415 Page 4 of 8 Table 1 Outline of the recommendations of the health system’s opioid management policy Item Summary of the Policy-recommended Components Recommended components of opioid therapy management Problem List 1. Document diagnosis of chronic pain and source of pain 2. Document information related to relevant prescribed medications: a. Details of opioid prescription, with allowed quantity per given time period b. Name and location of designated pharmacy c. Date when treatment agreement was most recently signed d. Urine drug testing findings 3. PDMP review: date of last review, finding summary, e.g., consistent or inconsistent with prescription record 4. Document care plan 5. Add comments helpful to other providers, e.g., those covering in your absence 6. Update at least annually and when any changes occur Care Plan Components 1. Treatment goals: pain severity (BPI), function (BPI/other) 2. Treatment plan (medications, exercise, physical or occupational therapy, mental health related therapies, CAM therapies, specialty consults) 3. Contingency plan for care outside PCP office 4. Update at least annually and when any changes to care plan Treatment 1. Serves as informed consent to long-term opioid therapy Agreement 2. Scan new or updated signed treatment agreement into the EHR 3. Update treatment agreement annually and when any changes to care plan 4. Deactivate treatment agreement after opioids are no longer prescribed Urine Drug Testing 1. Complete urine drug testing annually or more frequently as needed 2. Perform confirmatory testing for unexpected results of a screening test 3. Document findings Prescription Refills 1. Prescription for controlled substances should be filled at one agreed upon pharmacy, which is noted in the treatment agreement 2. Prescriptions for Schedule II medications can be mailed to pharmacy only 3. Patient may sign a release form to designate up to 2 appointees who can pick up prescriptions for Schedule II medications with photo ID PDMP 1. Document findings of the PDMP database review at least annually. Approach to treatment agreement violation Minor Infractions 1. Patient should be contacted by prescribing provider; discussion documented 2. Reassess and update care plan and treatment agreement as needed Major Infractions Follow minor infraction steps above; in addition: 1. If opioid therapy is discontinued, provide, when appropriate: a. opioid taper instructions and prescription(s) to accomplish the taper b. prescriptions for non-opioid medications for opioid withdrawal symptoms 2. Document reason for the discontinuation of opioid therapy 3. Deactivate treatment agreement when opioid treatment is completed 4. Communicate with other treating clinicians 5. Contact Patient Relations; discuss placing a flag, if needed, in medical record by the Department of Pharmacy 6. Continue non-opioid treatment 7. If all care is planned to be terminated, discuss “No further service” with Patient Relations Suspected Misuse or 1. Consider referring to addiction medicine specialist Use Disorder 2. If safe, continue modified or current opioid therapy until plan is in place with addiction specialist 3. Consider following the steps as for major violation of the treatment agreement BPI Brief Pain Inventory, CAM Complementary and Alternative Medicine, EHR Electronic Health Record, PCP Primary Care Provider, PDMP Prescription Drug Monitoring Program Zgierska et al. BMC Health Services Research (2018) 18:415 Page 5 of 8 Table 2 The intervention for augmenting routine health system-based implementation of opioid policy recommendations in primary care QI Intervention Component Description Academic Detailing A single on-site educational meeting between a content expert (project team member) and the clinicians and staff from the enrolled clinic wishing to improve the quality of care for their opioid-treated patients. Two Online Educational Modules, Brief, straightforward, and easily accessible educational tools delivered via the web or mobile devices. delivered via email: A set of 20–21 multiple-choice questions with instant feedback allows learners to assess and validate 1) Responsible Opioid Prescribing their current knowledge of the targeted content, which is presented in the context of a given health 2) Shared Decision Making system setting. These modules were developed by the project team members, content area experts, and reviewed by the health system and external experts (content can be made available upon request). Practice Facilitation An evidence-based method of assisting clinical practices in changing and optimizing the process of care. External facilitators (project team members) assist practices in implementing their prioritized goals and changing practice workflow, typically using the Plan, Do, Study, Act cycle model, () with the ultimate goal of improved patient care and outcomes. Two Patient Education Modules: Brief, online educational tools for patients, professionally developed by Emmi Solutions, 1) Opioids for Chronic Pain LLC (https://www.my-emmi.com/SelfReg/PAIN). 2) Agreement for Using Opioids outcomes and the need for modifications to the Participating clinicians and staff who complete all implemented processes. For this QI intervention, intervention components will receive 23 educational the project team developed materials pertinent to credits (American Medical Association Physician Recog- workflow optimization, including a summary of the nition Award Category 1); for those completing a part of health system’s opioid policy recommendations the intervention, the available credits will be prorated (Table 1), available EHR-based tools (e.g., according to the documented participation. “smartsets,”“smartphrases”), and general workflow recommendations for policy adherence. The PF por- Outcome measures tion of the intervention includes four elements: 1) To evaluate the impact of the QI intervention, the pro- Four to six PF sessions held over a 3–6 month ject team will collect two main types of data before, dur- period with clinic staff representing all clinical roles ing and after the intervention: a) EHR-based clinic-level to identify opportunities and preferences for work- data on elements of the health system’s opioid policy; flow improvements. 2) Encouraging the use of the and b) process measures from the clinical staff, and pro- Plan, Do, Study, Act (PDSA) model  to discuss ject team experiences, and perceptions related to the QI and identify barriers, problem-solve, and summarize intervention implementation. the implementation of actionable goals through small-scale tests of change in workflows. The EHR-based Measures (Table 3) The health system’s identified changes are then implemented, and opioid policy contains numerous recommendations for discussed in the subsequent PF session. optimizing care for patients with opioid-treated chronic 3) Identifying clinic-wide tools for effective pain (Table 1). Although the policy did not comment on communication between staff members. 4) Utilizing opioid and benzodiazepine co-prescribing, we also chose clinic-level outcome data to provide feedback on to address this issue and track these data because of na- how the selected changes in workflow and clinical tional guideline recommendations against the combin- practices impact the clinic’s adherence to the opioid ation of such medications due to increased overdose risk policy elements. [14, 15]. Aggregate clinic-level data will be collected 4) Patient Education Materials Two patient education monthly on the EHR-based measures that are both clin- videos, developed by a patient engagement and ically important and reliably measured over time. Clin- education organization, were made available for all ical adherence to only a handful of recommendations clinics to provide to their patients: a five-minute related to opioid prescribing practices can be reliably video addressing treatment agreements, and a measured using the EHR data. Consistent with the 20-min video focusing on opioid therapy in chronic health system’s opioid policy recommendations, the pain . Through the PF sessions, each clinic change in the clinic-level percentage of signed treatment decided how to use the patient materials, such as agreements will serve as the primary outcome. While in- making them a part of the pre-clinic visit or patient dividual patient data on dispensed controlled substances rooming process, having the patient watch them at are not available for outcome evaluation through the home post-visit, or not use them at all. state PDMP database, we will measure the clinic-level Zgierska et al. BMC Health Services Research (2018) 18:415 Page 6 of 8 Table 3 Measures to evaluate the implementation of guideline and health system’s opioid management policy recommendations Evaluation Component Clinic-Level Measures Clinically-Relevant Outcomes EHR-based Measures (aggregate clinic-level data) Treatment Agreement Percent of eligible patients with signed treatment agreement in the past 12 months. Urine Drug Testing Percent of eligible patients with the health system-recommended urine drug testing completed in the past 12 months. Opioid Therapy Risk Assessment Percent of eligible patients with documented screening using the health system-recommended D.I.R.E. opioid misuse risk tool. Depression Screening Percent of eligible patients with documented screening using the health system-recommended PHQ-2 or − 9 depression screening tool. b a Co-prescription of Opioids and Benzodiazepines Percent of eligible patients with presence of active prescriptions for both opioids and benzodiazepines. PDMP Check Percent of eligible patients with documented PDMP database check in the past 12 months. Process Measures (aggregate clinic-level data) Clinic Team Surveys Pre- and post-participation surveys will elicit: 1) ordinal responses as well as semi-qualitative comments to questions about current practice patterns; 2) comfort level with selected aspects of care for patients with opioid-treated chronic pain; 3) usefulness of the QI intervention components (post-participation). Clinic Team Member Participation Percent of clinicians and clinical staff per clinic who: in the Intervention Components - participated in the academic detailing session - enrolled in and completed each of the two online educational modules - participated in the practice facilitation sessions Data from Practice Facilitators Practice facilitator notes and experiences will enable identification of themes relevant to the implementation of the opioid policy (barriers and facilitators). D.I.R.E Diagnosis, Intractability, Risk, Efficacy assessment tool, QI Quality Improvement, PDMP Prescription Drug Monitoring Program, PHQ Patient Health Questionnaire Target population: health system’s primary care adult (18 years old or older) patients treated with long-term opioids for chronic non-cancer pain. To be included in the analysis, patients must have met the following criteria: age ≥ 18 years old; active patient status (seen in the past 3 years) in the health system’s January 2016 panel data; have a primary care provider at the health system’s general internal medicine or family medicine clinics; do not have a diagnosis of malignant neoplasm (except non-melanoma skin cancer) or hospice status; and meet at least one of the two health system’s “opioid registry” criteria: Criterion 1: have at least one opioid prescription issued in the prior 45 days AND at least three opioid prescriptions issued in the prior 4 months; Criterion 2: have at least one opioid prescription issued in the prior 45 days, AND chronic pain diagnosis listed, AND a controlled substance agreement This element, although included in the opioid prescribing guidelines, was not a part of the health system’s policy on opioid therapy management rate of clinician/delegates signing into the PDMP, based in 2014 and on a cluster randomized trial methodology, with on documentation of the PDMP check in the EHR. We an intra-class correlation coefficient of 1.5%. These calcula- will also assess selected EHR-based data on clinic- and tions estimated the project would have 84% power and over clinician-level characteristics as covariates (e.g., FM/GIM, 95% confidence to detect a 20% relative increase in use of community/residency clinics, and patient panel size). treatment agreements (primary outcome) over time . A 20%increaseisconsistentwithexpertrecommendations for Process Measures (Table 3) Clinician and clinic staff en- measurement of a minimal clinically important difference gagement and experience will be assessed through: A) ; given the short timeline of our intervention, even a quantitative and qualitative answers to pre- and minimal difference could suggest a meaningful change. post-participation questionnaires, developed by the pro- Longitudinal changes in clinic-level EHR-based measures ject team (attached as an Additional file 1); B) prescriber will be assessed in the enrolled 9 clinics, as well as in the and clinical staff participation in the QI intervention com- remaining 19 primary care clinics not enrolled in the QI ponents (session attendance; enrollment in and comple- project. The analysis of within- and between-clinic changes tion of the online educational modules); and C) qualitative and experience with the intervention (process measures) assessment of the experiences and perspectives of practice will enable an evaluation of the intervention’s effects. facilitators. In addition, we will also explore the number of log-ins into the online patient education tools. Discussion This paper describes the development of a multi-pronged, Statistical analysis tailored QI intervention aimed at augmenting the Sample size and power calculations were based on the health system-wide implementation of policy and guidelines on system’s EHR data from when the project was first planned opioid therapy management in chronic non-cancer pain. Zgierska et al. BMC Health Services Research (2018) 18:415 Page 7 of 8 We hypothesize that the addition of this intervention will Acknowledgements The authors would like to thank Drs. June Dahl, Nathan Rudin, Rodney enhance implementation of guideline-driven recommenda- Erickson and France Légaré for reviewing and providing feedback on the tions in primary care, as compared to a “routine,” unen- content of educational modules and other components of the intervention, hanced policy rollout in a large health system. Rigorous and the University of Wisconsin-Madison (UW) Health administrative and clin- ical leadership and the Center for Clinical Knowledge Management for evaluation of the effects of this intervention will be reported making this project possible. in a future publication. Lack of efficient translation of research findings into Availability of data and materials Data sharing is not applicable as no datasets were generated or analyzed for routine practice is a common obstacle to improving the the current manuscript. The content of online educational materials can be quality of care . This may be particularly true for com- made available upon request. plex recommendations, such as those on opioid therapy Funding management. Underutilization of opioid guidelines has This work was supported by an unrestricted researcher-initiated grant from Pfizer been documented  and supported by our data on the (#16213567). Dr. Zgierska’s effort was additionally supported by the K23AA017508 baseline adherence of primary care clinicians to selected grant from the National Institutes of Health (NIH) National Institute on Alcohol Abuse and Alcoholism (NIAAA) and funds from the University of Wisconsin- recommendations. Closing the gap between knowledge Madison. The funding organizations had no role in the design and conduct of the (guidelines) and practice can improve patient care and study; collection, management, analysis, and interpretation of the data; outcomes, which, in this case, could lead to curtailing the preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. impact of opioid use disorders and overdose deaths [5–8]. It is not currently clear which methods are most effective Authors’ contributions for promoting system-level learning, change and QI in rou- All authors substantially contributed to the work described in this manuscript, tine clinical care. A systematic and rigorous outcome assess- read and approve of the final version of the manuscript. AEZ contributed to project conception, design, and conduct, and drafted the manuscript; all other ment of QI efforts — such as those this project team co-authors (RMV, PS, MWA, KN, DB, WJT, DLH) contributed to project conception, proposes — is essential for discerning whether interventions design and execution, and edited the manuscript. with intuitive appeal actually result in desired change . If Authors’ information the proposed enhanced efforts do not produce better out- AEZ is an Assistant Professor (tenure track) who is board-certified in family medicine comes than “routine” efforts, the health system is justified in and addiction medicine, and provides both primary and specialty care; her research not investing in such activities. If, on the other hand, these focuses on improving care and outcomes in patients with opioid-treated chronic pain or opioid addiction. RMV is a doctorally-trained Program Manager for a primary augmented efforts improve outcomes, the health system will care research network with experience in primary care study design and be alerted to the fact that further investment in such imple- implementation. PDS is a Professor (clinical health sciences) who is board-certified in mentation, although more labor-intensive, has clinical value. family medicine, and provides primary care; his research centers on patient-provider communication. DLH is a senior scientist who is board-certified in family medicine Therefore, positive or negative results should yield valuable and directs a primary care research network. DB is a research coordinator at a information, promote system learning and change, and lay primary care research network with experience in practice facilitation. MWA directs the foundation to improve approaches to future system-wide and KN is a program manager at an organization with an over 100-year history of providing advanced medical education. WJT is a database analyst specializing in QI efforts. extracting complex clinical information from electronic health records. Conclusions Ethics approval and consent to participate Institutional Review Board review was not required because, in accordance Developing methods for a health system-tailored QI interven- with federal regulations, this was deemed a QI project, not constituting tion required a multi-step process to incorporate end-user human subjects research, as defined under 45 CFR 46.102(d). feedback and account for the needs of targeted clinic team members. Delivery of such tailored QI interventions has the Competing interests The authors declare that they have no competing interests. potential to enhance uptake of opioid therapy management policies in primary care. Results from this study are antici- Publisher’sNote pated to elucidate the relative value of such QI activities. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Additional file Author details Department of Family Medicine and Community Health, Wisconsin Research Additional file 1 Surveys of the Clinic Team Members: A. Pre- and Education Network, School of Medicine and Public Health, University of Participation; B. Post-Participation. (DOCX 57 kb) Wisconsin-Madison, 1100 Delaplaine Court, Madison, WI 53715, USA. Interstate Postgraduate Medical Association, P.O. Box 5474, Madison, WI 53705, USA. Abbreviations BPI: Brief Pain Inventory; CAM: Complementary and Alternative Medicine; Received: 4 May 2017 Accepted: 22 May 2018 D.I.R.E.: Diagnosis, Intractability, Risk, Efficacy assessment tool; EHR: Electronic Health Record; FM: family medicine; GIM: general internal medicine; MME: morphine milligram equivalent; PCP: Primary Care Provider; PDMP: Prescription Drug Monitoring Program; PDSA: Plan, Do, Study, Act References model; PF: Practice Facilitation;; PHQ: Patient Health Questionnaire; 1. Institute of Medicine (IOM) of the National Academies. Relieving Pain in QI: Quality Improvement America: A Blueprint for Transforming Prevention, Care, Education, and Zgierska et al. BMC Health Services Research (2018) 18:415 Page 8 of 8 Research. https://www.ncbi.nlm.nih.gov/pubmed/27136641. Accessed 30 23. Woertman W, de Hoop E, Moerbeek M, Zuidema SU, Gerritsen DL, May 2018. Teerenstra S. Stepped wedge designs could reduce the required sample 2. Agency for Healthcare Quality and Research. 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BMC Health Services Research – Springer Journals
Published: Jun 5, 2018
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