A Pathway for Developing Postoperative Opioid Prescribing Best Practices

Ryan Howard, MD; Joceline Vu, MD; Jay Lee, MD; Chad Brummett, MD; Michael Englesbe, MD; Jennifer Waljee, MD, MS, MPH

Disclosures

Annals of Surgery. 2020;271(1):86-93. 

In This Article

Abstract and Introduction

Abstract

Objective: Opioid prescriptions after surgery are effective for pain management but have been a significant contributor to the current opioid epidemic. Our objective is to review pragmatic approaches to develop and implement evidence-based guidelines based on a learning health system model.

Summary Background Data: During the last 2 years there has been a preponderance of data demonstrating that opioids are overprescribed after surgery. This contributes to a number of adverse outcomes, including diversion of leftover pills in the community and rising rates of opioid use disorder.

Methods: We conducted a MEDLINE/PubMed review of published examples and reviewed our institutional experience in developing and implementing evidence-based postoperative prescribing recommendations.

Results: Thirty studies have described collecting data regarding opioid prescribing and patient-reported use in a cohort of 13,591 patients. Three studies describe successful implementation of opioid prescribing recommendations based on patient-reported opioid use. These settings utilized learning health system principles to establish a cycle of quality improvement based on data generated from routine practice. Key components of this pathway were collecting patient-reported outcomes, identifying key stakeholders, and continual assessment. These pathways were rapidly adopted and resulted in a 37% to 63% reduction in prescribing without increasing requests for refills or patient-reported pain scores.

Conclusion: A pathway for creating evidence-based opioid-prescribing recommendations can be utilized in diverse practice environments and can lead to significantly decreased opioid prescribing without adversely affecting patient outcomes.

Introduction

Opioid prescribing following surgical care has been a major factor driving the opioid epidemic in the United States.[1] Excessive opioid prescribing has commonly occurred across surgical procedures and specialties, in part because of a lack of evidence about patients' opioid requirements following surgery.[2–6] Unfortunately, unused pills following procedural care are a major source of nonmedical opioid use, and the most common initial opioid exposure for individuals with opioid use disorder owing to heroin.[7–11] Moreover, high levels of opioid prescribing in the immediate postoperative period are associated with prolonged opioid use among previously opioid-naïve patients.[12–15] Although recent legislative measures now require providers to use prescription drug monitoring programs (PDMPs) and restrict prescribing for acute pain, these have been met with mixed success.[16–19] Furthermore, these measures are neither patient-focused nor physician-driven, and fail to engage these primary stakeholders in the current opioid crisis.

A number of institutions have had success reducing excessive opioid prescribing utilizing a quality improvement framework.[20–23] This framework is based on the principles of a learning health system, in which improvements in care are achieved by integrating data and experience generated by routine practice.[24] In this model, a continuous cycle is developed in which data are analyzed to identify opportunities for quality improvement, measures are implemented based on this analysis, and then iterative change takes place based on reanalysis of new information (Figure 1). A well-known example of this is the development of a National Trauma Care System with the goal of zero preventable deaths for patients who have sustained traumatic injury.[25] It is estimated that this effort has the potential to save 100,000 lives in a 5-year period.[26] Another successful example includes the Surgical Care and Outcomes Assessment Program, wherein electronic medical records are used to feedback outcomes and performance to surgeons in the state of Washington. This has resulted in decreased variability in care delivery, which in turn has decreased complications and improved cost savings.[27]

Figure 1.

Cycle of continuous quality improvement in a learning health system.

For opioid prescribing, similar projects have leveraged patient feedback on key outcomes, such as pain, satisfaction, and opioid consumption to create tailored guidelines for postoperative opioid prescribing.[28] Encouragingly, this work has led to significant and sustained improvements in postoperative prescribing without negatively impacting patient satisfaction or pain.[20,29,30] Randomized controlled trials (RCTs) could help augment these studies and establish a causal connection between prescribing recommendations based on patient outcomes, changes in prescribing practice, and stability in patient satisfaction. However, RCTs are costly, logistically challenging to execute, and often do not represent real-world practice given the constraints of creating study cohorts and comparison groups.[31] In addition, it is imperative to ensure pain is effectively treated after surgery, and testing interventions that threaten adequate postoperative pain control can be ethically challenging.[32]

In the absence of high-level evidence, pragmatic quality improvement studies based on the principles of a learning health system may represent the best opportunity to garner robust, patient-centered evidence to efficiently change practice. We review a pathway that is meant to engage surgeons and patients in generating data to support changes in prescribing practice that are unique to the settings in which they are implemented. By utilizing analysis and feedback from providers and patients, practice change is more likely to be sustainable and reflect the values of patients and providers.[33,34] The result is rapid improvement in quality of surgical care that integrates patient-reported outcomes to create practical opioid prescribing guidelines following surgery.

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