Stepped care model of pain management and quality of pain care in long-term opioid therapy

A Systematic Review of Chronic Pain Management Interventions Among Veterans of Recent Wars and Armed Conflicts

OBJECTIVES

To identify chronic pain management strategies aimed to reduce pain intensity and enhance functional outcomes in veterans of wars and armed conflict.

DESIGN

Systematic review without meta-analysis.

DATA SOURCES

Key words "chronic pain," "veterans," and "injuries" were used to search for articles in the MEDLINE, CINAHL, APA PsycInfo, and Embase databases. Articles published in English between 2000 and 2023 were included.

REVIEW/ANALYSIS METHODS

A systematic literature search was conducted in June 2020, updated in April 2023, and managed using Covidence review software. Inclusion criteria focused on combat-injured veterans with chronic pain, excluding nonveterans and civilians treated for acute or chronic pain. Data from included studies were extracted, summarized, and critically appraised using the 2018 Mixed Methods Appraisal Tool. This review is registered with PROSPERO (CRD42020207435).

RESULTS

Fourteen studies met the inclusion criteria, with 10 of them supporting nonpharmacological approaches for managing chronic pain among veterans of armed conflicts and wars. Interventions included psychological/behavioral therapies, peer support, biofeedback training via telephone-based therapy, manual therapy, yoga, cognitive processing therapy, cognitive-behavioral therapy, and social and community integration to reduce pain intensity and enhance functional outcomes.

CONCLUSION

Nonpharmacological treatments for chronic pain have increased in recent years, a shift from earlier reliance on pharmacological treatments. More evidence from randomized controlled trials on the benefits of combined pain interventions could improve pain management of veterans with complex care needs.

Measuring pain care quality in the Veterans Health Administration primary care setting

ABSTRACT

The lack of a reliable approach to assess quality of pain care hinders quality improvement initiatives. Rule-based natural language processing algorithms were used to extract pain care quality (PCQ) indicators from documents of Veterans Health Administration primary care providers for veterans diagnosed within the past year with musculoskeletal disorders with moderate-to-severe pain intensity across 2 time periods 2013 to 2014 (fiscal year [FY] 2013) and 2017 to 2018 (FY 2017). Patterns of documentation of PCQ indicators for 64,444 veterans and 124,408 unique visits (FY 2013) and 63,427 veterans and 146,507 visits (FY 2017) are described. The most commonly documented PCQ indicators in each cohort were presence of pain, etiology or source, and site of pain (greater than 90% of progress notes), while least commonly documented were sensation, what makes pain better or worse, and pain's impact on function (documented in fewer than 50%). A PCQ indicator score (maximum = 12) was calculated for each visit in FY 2013 (mean = 7.8, SD = 1.9) and FY 2017 (mean = 8.3, SD = 2.3) by adding one point for every indicator documented. Standardized Cronbach alpha for total PCQ scores was 0.74 in the most recent data (FY 2017). The mean PCQ indicator scores across patient characteristics and types of healthcare facilities were highly stable. Estimates of the frequency of documentation of PCQ indicators have face validity and encourage further evaluation of the reliability, validity, and utility of the measure. A reliable measure of PCQ fills an important scientific knowledge and practice gap.

Development and implementation of a primary care clinic workflow protocol to meet opioid prescribing guidelines

BACKGROUND

Nearly 50% of opioid overdose deaths in the United States involve the use of prescription opioids. Primary care providers can help decrease the risk of opioid overdose deaths by adhering to opioid prescribing guidelines for chronic pain management.

LOCAL PROBLEM

Ten Washington State primary care clinics had gaps in guideline adherence and mandated electronic medical record (EMR) documentation for prescribing opioids.

METHODS

A quality improvement project using an educational intervention was implemented.

INTERVENTIONS

Primary care providers and support staff (defined as registered nurses and medical assistants) from the 10 primary care clinics viewed the project's instructional YouTube webinar that explained the project's primary care clinic workflow protocol, opioid prescribing best practice guidelines, and the organization's mandated EMR charting for chronic pain management. Preintervention and postintervention measures, which included five different documented patient completion rates of the organization's best practices for opioid prescribing, were used to assess for improvement to guideline adherence. Additionally, participants completed a questionnaire regarding their perceptions of the webinar as an educational tool.

RESULTS

Postintervention data showed significantly (p ≤ .05) increased completion rates for three of five outcome measures, indicating improvement in guideline adherence. The majority of participants reported that the webinar information would help them better adhere to best practice guidelines.

CONCLUSIONS

A workflow protocol for opioid prescribing taught via a YouTube webinar was an effective method for disseminating and implementing best practices in the primary care setting. Similar workflow protocols, taught via webinar, could be equally beneficial in other primary care clinics.

Project STEP: Implementing the Veterans Health Administration's Stepped Care Model of Pain Management

Objective

The "stepped care model of pain management" (SCM-PM) prioritizes the role of primary care providers in optimizing pharmacological management and timely and equitable access to patient-centered, evidence-based nonpharmacological approaches, when indicated. Over the past several years, the Veterans Health Administration (VHA) has supported implementation of SCM-PM, but few data exist regarding changes in pain care resulting from implementation. We examined trends in prescribing and referral practices of primary care providers with hypotheses of decreased opioid prescribing, increased nonopioid prescribing, and increased referrals to specialty care for nonpharmacological services.

Design

An initiative was designed to foster implementation and systematic evaluation of the SCM-PM over a five-year period at the VA Connecticut Healthcare System (VACHS) while fostering collaborative, partnered initiatives to promote organizational improvements in the delivery of pain care.

Subjects

Participants were veterans receiving care at VACHS with at least one pain intensity rating ≥4/10 over the course of the study period (7/2008-6/2013).

Methods

We used electronic health record data to examine changes in indicators of pain care including pharmacy and health care utilization data.

Results

We observed hypothesized changes in long-term opioid and nonopioid analgesic prescribing and increased utilization of nonpharmacological treatments such as physical therapy, occupational therapy, and clinical health psychology.

Conclusions

Through a multifaceted comprehensive implementation approach, primary care providers demonstrated increases in guideline-concordant pain care practices. Findings suggest that engagement of interdisciplinary teams and partnerships to promote organizational improvements is a useful strategy to increase the use of integrated, multimodal pain care for veterans, consistent with VHA's SCM-PM.

Comorbid chronic pain and opioid use disorder: literature review and potential treatment innovations

Chronic pain (CP) and opioid use disorder (OUD) remain challenging complex public health concerns. This is an updated review on the relationship between CP and OUD and the use of stepped care models for assessment and management of this vulnerable population. A literature search was conducted from 2008 to the present in PubMed, Embase, and PsycInfo using the terms pain or chronic pain and opioid-related disorders, opiate, methadone, buprenorphine, naltrexone, opioid abuse, opioid misuse, opioid dependen*, heroin addict, heroin abuse, heroin misuse, heroin dependen*, or analgesic opioids, and stepped care, integrated services, multidisciplinary treatment, or reinforcement-based treatment. Evidenced-based data exists on the feasibility, implementation, and efficacy of stepped care models in primary care settings for the management of CP and opioid use. Although these studies did not enroll participants with OUD, they included a sub-set of patients at risk for the development of OUD. There remains a dearth of treatment options for those with comorbid CP and OUD. Future research is needed to explore the aetiology and impact of CP and OUD, and greater emphasis is needed to improve access to comprehensive pain and substance use programmes for high-risk individuals.

Classifying clinical notes with pain assessment using machine learning

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR). Hereby, we used unstructured text narratives in the EHR to derive pain assessment in clinical notes for patients with chronic pain. Our dataset includes patients with documented pain intensity rating ratings > = 4 and initial musculoskeletal diagnoses (MSD) captured by (ICD-9-CM codes) in fiscal year 2011 and a minimal 1 year of follow-up (follow-up period is 3-yr maximum); with complete data on key demographic variables. A total of 92 patients with 1058 notes was used. First, we manually annotated qualifiers and descriptors of pain assessment using the annotation schema that we previously developed. Second, we developed a reliable classifier for indicators of pain assessment in clinical note. Based on our annotation schema, we found variations in documenting the subclasses of pain assessment. In positive notes, providers mostly documented assessment of pain site (67%) and intensity of pain (57%), followed by persistence (32%). In only 27% of positive notes, did providers document a presumed etiology for the pain complaint or diagnosis. Documentation of patients' reports of factors that aggravate pain was only present in 11% of positive notes. Random forest classifier achieved the best performance labeling clinical notes with pain assessment information, compared to other classifiers; 94, 95, 94, and 94% was observed in terms of accuracy, PPV, F1-score, and AUC, respectively. Despite the wide spectrum of research that utilizes machine learning in many clinical applications, none explored using these methods for pain assessment research. In addition, previous studies using large datasets to detect and analyze characteristics of patients with various types of pain have relied exclusively on billing and coded data as the main source of information. This study, in contrast, harnessed unstructured narrative text data from the EHR to detect pain assessment clinical notes. We developed a Random forest classifier to identify clinical notes with pain assessment information. Compared to other classifiers, ours achieved the best results in most of the reported metrics. Graphical abstract Framework for detecting pain assessment in clinical notes.

Improving pain care through implementation of the Stepped Care Model at a multisite community health center

PURPOSE

Treating pain in primary care is challenging. Primary care providers (PCPs) receive limited training in pain care and express low confidence in their knowledge and ability to manage pain effectively. Models to improve pain outcomes have been developed, but not formally implemented in safety net practices where pain is particularly common. This study evaluated the impact of implementing the Stepped Care Model for Pain Management (SCM-PM) at a large, multisite Federally Qualified Health Center.

METHODS

The Promoting Action on Research Implementation in Health Services framework guided the implementation of the SCM-PM. The multicomponent intervention included: education on pain care, new protocols for pain assessment and management, implementation of an opioid management dashboard, telehealth consultations, and enhanced onsite specialty resources. Participants included 25 PCPs and their patients with chronic pain (3,357 preintervention and 4,385 postintervention) cared for at Community Health Center, Inc. Data were collected from the electronic health record and supplemented by chart reviews. Surveys were administered to PCPs to assess knowledge, attitudes, and confidence.

RESULTS

Providers' pain knowledge scores increased to an average of 11% from baseline; self-rated confidence in ability to manage pain also increased. Use of opioid treatment agreements and urine drug screens increased significantly by 27.3% and 22.6%, respectively. Significant improvements were also noted in documentation of pain, pain treatment, and pain follow-up. Referrals to behavioral health providers for patients with pain increased by 5.96% (P=0.009). There was no significant change in opioid prescribing.

CONCLUSION

Implementation of the SCM-PM resulted in clinically significant improvements in several quality of pain care outcomes. These findings, if sustained, may translate into improved patient outcomes.