Introduction

The opioid epidemic continues across the United States, accounting for 47,600 overdose deaths (67.8% of all overdose deaths) in 2017 [1]. While illicit drugs, such as heroin, account for many deaths, available data show that up to 82.6% of opioid drug users start by using a prescription medication [2], placing the epidemic squarely within the scope of healthcare industries. In addition to the staggering mortality, opioid use disorder causes significant morbidity to patients as well as financial impact in both healthcare expense and productivity losses accounting for a combined annual cost of $504 billion to American society [3].

Opioid medications were historically thought to be low risk for misuse or addiction and were marketed as such to prescribers for many years [4, 5]. However, more recent and robust evidence shows that patients, including those who are opioid naïve, are at significant risk of developing long term opioid use after even a single prescription [6–8]. This risk for patients developing long term use varies based on the clinical setting in which medications are prescribed and the indication for the use of the medication, where the risk of developing long-term use ranges from 2% to as high as 30% [9–11], with about 6% of previously opioid naïve surgical patients developing chronic opioid use post-operatively [6, 7].

Despite significant attention to opioid prescribing, including legislation in several states, most systems have only shown modest decreases in opioid prescribing [11, 12]. Interestingly, patients tend to need significantly fewer doses of opioid medications than we often prescribe for their acute pain, suggesting that prescribing habits are driven by historical practices and not patients’ use patterns [13–17]. Such excess prescribing increases availability of medications for diversion and/or misuse. Indeed, most drug diversion and misuse is of lawfully obtained medications, and as many as 25% of opioids dispensed in the United States are used nonmedically [18]. Indeed, the amount of medication in a prescription is thought to be the greatest risk factor for opioid prescription misuse [19]. This suggests an opportunity for improvement, whereby bringing prescribing practices in line with patient needs can decrease the risk of misuse while adequately addressing patients’ pain management.

Ever since the Institute of Medicine’s publication of To Err is Human (2000) and Crossing the Quality Chasm (2001), healthcare systems have endeavored to bring quality improvement methodologies from other industries to healthcare settings. The DMAIC (Define, Measure, Analyze, Improve and Control) method, popularized by Six Sigma projects, is frequently used to achieve goals in individual healthcare units, hospitals/clinics, and health systems. Moving beyond individual disciplines and institutions, The Institute for Healthcare Improvement (IHI) developed the “Breakthrough Series,” a collaborative learning model for addressing difficult problems in healthcare. Early on, this model successfully improved cesarean section rates, waiting times in clinics, and asthma care utilization at several institutions [20]. The Mayo Clinic Care Network (MCCN) was launched in 2011 as a group of like-minded organizations with the goal of optimizing patient-centered care without respect to geography. The MCCN now has 45 members in 9 countries, representing 96 hospitals, over 18,000 physicians, and has the potential to reach more than 17 million patients. The MCCN has successfully used a shared learning and expertise model for collaborative improvements among multiple health systems in the past for improvements such as enhanced recovery following colon and rectal surgery [21, 22] and others as well. MCCN members are invited to participate in such collaboratives, including the one we report here: improving acute opioid prescribing. We aim to describe our experience with this collaborative in order to inform future initiatives aimed at reducing opioid prescribing and demonstrate the utility of a collaborative in these efforts.

Methods

MCCN members were invited to participate in a quality improvement collaborative to improve opioid prescribing. Over the course of a year, Mayo Clinic participants facilitated goal setting and implementation of quality improvement projects at each institution to reduce opioid prescribing. Each member organization conducted an improvement project over 12 months with monthly coaching calls including subject matter experts and quality improvement specialists from the collaborative. Participating organizations met both face to face and via web meetings to discuss their progress and challenges. Here we detail the actions that took place during each stage of this collaborative work.

Collaborative participants

Nine MCCN health systems from across the United States chose to participate in the acute opioid prescribing reduction collaborative. These nine systems represented both single and multihospital organizations serving patients in urban, suburban, and rural settings representing a combined 4,262 physicians serving a population of 2,876,850 patients (Table 1). The collaborative took place over 12 months with frequent and significant interaction between participants and Mayo Clinic staff (Fig 1, details in S2 Table).

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Fig 1. Project timeline.

https://doi.org/10.1371/journal.pone.0270179.g001

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Table 1. Participating institutions.

https://doi.org/10.1371/journal.pone.0270179.t001

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Table 2. Opioid prescribing improvement data.

https://doi.org/10.1371/journal.pone.0270179.t002

Institutional project design

During the first face-to-face meeting, each participating team identified their goal and target population for their opioid reduction efforts. Each institution set their own goals, most of which were quantitative improvements in outpatient prescribing, although some chose to focus on qualitative improvements without a quantitative target. Of nine participating organizations, one elected to focus on emergency department (ED) based prescribing, focusing on prescribing for patients with the chief complaint of back pain. The remaining eight members focused on reducing postoperative opioid prescribing following a variety of surgical procedures, including orthopedic, spinal, vascular, gynecologic, gastrointestinal, colorectal, and hepatobiliary procedures. Most teams chose to focus on one or two patient populations (eg, joint revision and hysterectomy), while some expanded their goals across many surgical specialties (Table 1). The availability of published evidence on post-surgical prescribing practices motivated many of the organizations to pursue reductions in the amount of opioids prescribed, while other organizations focused on variability that they saw in their prescribing data, and others were motivated by perceived gaps in the current practice.

Participating teams employed a variety of interventions to achieve their aims. Interventions included implementing standard prescribing guidelines, protocols, and order sets; developing patient education materials focused on patients both at admission to set expectations and at discharge to provide support in home management and disposal; implementing post-discharge follow-up phone calls by care team members; and carrying out community education sessions and drug take-back events. Interventions focused on engaging leadership and staff included identifying organizational champions, developing multidisciplinary teams to lead prescribing change efforts, and disseminating comprehensive education content for providers, advanced practice leaders, pharmacists, nursing, and organizational leadership. Finally, interventions focused on information technology (IT) and the electronic health record (EHR) included standardizing electronic ordering processes, deploying IT and EHR builds for change requirements, and establishing data management and monitoring plans. As healthcare systems are complex, institutions employed a variety of methods in combination based on the needs of their individual cultures and goals. These tools were provided to them and assisted throughout the collaborative timeframe (Table 3).

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Table 3. Quality improvement interventions.

https://doi.org/10.1371/journal.pone.0270179.t003

Results

Discussion

This collaborative model of shared expertise and learning between institutions was successful at helping individual health systems complete concrete objectives, leading to reductions in opioid prescribing more accurately mirroring patient need. This brought about change for individual patients from projects supported on a national scale. Importantly, these interventions are customizable, as they are built on each institution’s goals and culture, portable, based on external collaboration and input, and also scalable, given similarities in post-operative and emergency care across hospitals.

Lessons learned from this collaborative may inform other health care administrators and quality improvement professionals on the utility of a collaborative in reducing opioid prescribing. Using a collaborative approach in these efforts was demonstrably successful; the Mayo Clinic Care Network is a collective entity of like healthcare organizations which benefit from a shared experience, and working together to identify and employ strategies yielded a greater impact on a higher number of patients. Furthermore, our efforts focused on clinically-based evidence, and interventions were built upon the demonstrated success of strategies previously implemented at Mayo Clinic. The varied interventions employed by the participating institutions may inform future efforts elsewhere.

This collaborative was successful due to several factors, but there are two that we feel should be emphasized. The first factor is the reliance on shared expertise and accountability. Between organizations, quality improvement experts partnered with bedside clinical staff with shared understanding of the needs of the clinical practice. This brings the expertise needed for implementing change successfully. Beyond that, however, collaborative participants held one another accountable both within organizations’ internal teams, as well as with Mayo Clinic staff who followed up with the teams on regular coaching calls and with data submission dates expected. Second, these projects were well supported by health systems’ leadership for both prioritization and resources. Support was ‘top down’ since the health systems’ leaders chose the collaborative teams, and asked them to participate in this project. Such endorsement was key in making changes, some of which required information technology support for dashboards or clinical decision support, acquiring funding, or staff time for training and participation. This level of support, or internal backing of the teams, was key to their successes.

Of the participant organizations, one healthsystem (I) made improvements and by the end of the collaborative had lost some of the gains they noticed initially. While the reasons for this are unclear in this particular circumstance, it raises the issue of sustainability. Control plans were presented and discussed throughout the collaborative to help foster long term improvements. There are likely also societal contributions to sustainability for opioid prescribing reductions since the opioid epidemic in the United States is well known at this point. Additionally, the focus of legislation on prescribing guidelines and monitoring initiatives in many states could help continue to prioritize these improvements.

Making improvements in opioid prescribing is challenging, and many factors affect prescribing that may confound our results. Certainly, the opioid epidemic has received a lot of attention during recent years and there may be a Hawthorne effect that could either bolster our efforts at decreasing prescribing or independently be influencing prescribers to decrease their opioid prescribing. Available evidence for this is unclear. Some data say that opioid prescribing is decreasing [12, 23], while other research has shown little to no effect in recent years [11].

Excellent pain control is needed when patients are suffering. Indeed, alleviation of that suffering is a core value of the field of medicine. Opioid medications are still often needed for treatment of the most severe pain. The strategies employed by this collaborative were to “right size” prescriptions where opioids were needed and not to prescribe opioids if they were not needed. In clinical situations where opioids were not thought to be needed, alternative pain control still needs to be provided whether that is through medications, procedures, or complementary therapy. The collaborative dedicated a webinar to alternative treatment options, but was not prescriptive in how to employ opioid alternatives. This is because interventions were intended to be pragmatic and customizable to both the health systems and patient populations targeted. While some health systems chose to focus on postoperative pain from significant procedures, others looked at lower acuity complaints such as idiopathic back pain. Clearly, a one-size-fits-all approach would not be possible for those very different populations. However, any group looking to shift therapy away from opioids should proactively consider alternative pain treatments as a key counterbalance measure to any opioid reduction quality improvement.

State legislation on opioid prescribing, particularly in those states pursuing prescribing limits, likely confounds our results somewhat. These legislative limits come in one of two general varieties: those that limit ‘days’ of medication prescribed, and those that limit amount by OME. Of these two general options, those that limit to ‘days’ of medication prescribed would complicate our results less so than those limiting OME. This is because of both how patients take a given medication and how prescribers write a prescription. Opioid medications are not intended to be taken all day, every day, but only when needed, so a supply measured in ‘days’ is significantly higher than many of our projects’ goals.

Several barriers are perceived in improving opioid prescribing. Many prescribers remain concerned that there may be decreased patient satisfaction when the amount of opioids prescribed is decreased. However, the literature available around this issue has not yet found that to be true [24]. Minimizing clerical burden is a top priority to combat burnout in medicine and additional requests on providers are to be actively avoided [25]. Previous work on post-operative prescribing reductions have not increased the rate of refill requests [17, 26], and post-visit phone calls from one of the collaborative members (Health System A) confirmed that patients indeed used less medication than prescribed. This would suggest that opioid reducing interventions were associated with good pain control following knee and hip replacement surgery, the chosen intervention population for health system A. However, none of the participant institutions directly measured patient reported pain control, which is a potential limitation of our study. As with any major change in clinical practice, ‘buy-in’ at all levels of care delivery is a concern for change leaders. This concern was expressed by all collaborative participants during the first face-to-face session. At the completion of the collaborative, six of the nine health systems specifically listed ‘buy-in’ and ‘engagement’ as something that “went well” for their projects, indicating that staff and leadership are likely more engaged with opioid improvement efforts than is perceived.

Our experiences presented here demonstrate the continuing utility of the IHI collaborative learning model approach in working towards the optimization of acute opioid prescribing across a range of settings and states with varying laws and restrictions. We learned several important lessons that will inform future work. First, acknowledging that there are significant, intrinsic differences between health systems is necessary. Goals of individual health care organizations vary, and hospitals and clinics are subject to different facilitators and challenges based on geographic and economic pressures within an area and health care market. In many ways, a smaller community setting may facilitate implementing change when systems are in a limited market; however, such places may not have the resources to bring improvement efforts to bear that larger institutions may have. Next, while much of this work is reproducible, the specific expertise and passion of individuals within any collaborative model cannot be fully replicated based on individuals’ strengths and talents. This collaborative is large enough that gaps are likely minimized. However, in order to repeat this process, attention to such talents or maintaining the critical mass of a collaborative may be needed to achieve these results. Lastly, the ability to scale and diffuse interventions is paramount in any challenging improvement development.

As the opioid epidemic continues, it remains paramount that health care institutions implement efforts to reduce levels of and variation in opioid prescribing. Our experiences demonstrate that the strengths of a collaborative representing diverse clinical practices are the breadth of expertise and varied solutions brought to bear, which may result in demonstrable impact to patient care and outcomes.

Supporting information

Acknowledgments

All of the members of the participating collaborative teams, who brought amazing expertise and leadership to the collaborative effort (S1 Table).