Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

Opioid Prescribing for Chronic Musculoskeletal Conditions: Trends over Time and Implementation of Safe Opioid-Prescribing Practices

OBJECTIVES

This study aimed (1) to determine the impact of a clinical decision support (CDS) tool on rate of opioid prescribing and opioid dose for patients with chronic musculoskeletal conditions and (2) to identify prescriber and facility characteristics associated with adherence to the Centers for Disease Control and Prevention guideline for prescribing opioids for chronic pain in this population.We conducted an interrupted time series analysis to assess trends in percentage of patients from 2016 to 2020, receiving an opioid and the average opioid dose, as well as the change associated with implementation of the CDS toolkit. We conducted a retrospective cohort study to assess the association between prescriber and facility characteristics and safe opioid-prescribing practices.

METHODS

We assessed the impact of the CDS intervention on percent of patients receiving an opioid and average opioid dose (morphine milligram equivalents). We operationalized safe opioid prescribing as a composite score of several behaviors (i.e., prescribing naloxone, initiating a pain agreement, prescribing <90 MME, avoiding extended-release prescriptions for opioid-naïve patients, and avoiding coprescribing opioids and benzodiazepines) and used a hierarchical linear regression model to assess associations between prescriber and facility characteristics and safe opioid prescribing.

RESULTS

This CDS intervention had a modest but statistically significant 1.6% reduction on the percent of patients (n = 1,290,746) receiving an opioid (mean: 15% preintervention; 10% postintervention). The average dose of opioid prescriptions did not significantly change. Advanced practice providers and prescribers with higher percentages of patients aged 18 to 64 exhibited safer opioid prescribing, while prescribers with higher percentages of white patients and larger numbers of patients on opioids exhibited less safe opioid prescribing.

CONCLUSION

A CDS intervention was associated with a small improvement in percent of patients receiving an opioid, but not on average dose. Clinicians are not prescribing opioids for chronic musculoskeletal conditions frequently, when they do, they are generally adhering to guidelines.

A Tool to Identify and Engage Patients on Risky Opioid Regimens

BACKGROUND

Concerns around opioid safety for patients living with chronic pain have led to a growing number of collaborative and multimodal pain care initiatives. A major challenge in these efforts has been identifying and engaging patients on high-risk opioid regimens in a timely manner.

OBJECTIVES

In this clinical informatics case report, we describe the development and implementation of a web-based tool to support providers as they implement an integrated pain support clinical initiative at primary care clinics across three health care systems.

METHODS

The tool identifies patients on risky opioid medication regimens and generates autopopulated patient outreach letters. It contains three core functions that: (1) identify patients prescribed high-dose opioids or coprescribed opioids and benzodiazepines, (2) generate automated letters for patients with an upcoming primary care appointment, and (3) allow clinic staff to write back to a database to track outreach and referrals. Qualitative stakeholder feedback was gathered through interviews and user testing to assess perceived usefulness and ease of use of the tool.

RESULTS

Over a 24-month period, the tool identified 1,125 patients prescribed risky medication regimens and generated 1,315 total letters as some patients became reeligible. Stakeholder feedback revealed that the tool was useful to quickly find patients on risky medication regimens and efficient in generating prepopulated letters that could be mailed in large batches. Additional feedback led to iterative refinements and improved system capabilities that varied across clinics.

CONCLUSION

Deploying clinical informatics tools that prioritize, engage, and track high-risk patient populations supports reduction of risky medication regimens. Such tools can reduce workload burden on busy primary care staff, particularly during implementation studies, and enhance patient-centered care through the use of direct-to-consumer outreach.