Operationalizing Multidisciplinary Assessment and Treatment as a Quality Metric for Interventional Pain Practices

Hierarchical clustering by patient-reported pain distribution alone identifies distinct chronic pain subgroups differing by pain intensity, quality, and clinical outcomes

Background

In clinical practice, the bodily distribution of chronic pain is often used in conjunction with other signs and symptoms to support a diagnosis or treatment plan. For example, the diagnosis of fibromyalgia involves tallying the areas of pain that a patient reports using a drawn body map. It remains unclear whether patterns of pain distribution independently inform aspects of the pain experience and influence patient outcomes. The objective of the current study was to evaluate the clinical relevance of patterns of pain distribution using an algorithmic approach agnostic to diagnosis or patient-reported facets of the pain experience.

Methods and findings

A large cohort of patients (N = 21,658) completed pain body maps and a multi-dimensional pain assessment. Using hierarchical clustering of patients by body map selection alone, nine distinct subgroups emerged with different patterns of body region selection. Clinician review of cluster body maps recapitulated some clinically-relevant patterns of pain distribution, such as low back pain with radiation below the knee and widespread pain, as well as some unique patterns. Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality all varied significantly across cluster subgroups. Multivariate modeling demonstrated that cluster membership independently predicted pain intensity and neuropathic pain quality. In a subset of patients who completed 3-month follow-up questionnaires (N = 7,138), cluster membership independently predicted the likelihood of improvement in pain, physical function, and a positive overall impression of change related to multidisciplinary pain care.

Conclusions

This study reports a novel method of grouping patients by pain distribution using an algorithmic approach. Pain distribution subgroup was significantly associated with differences in pain intensity, impact, and clinically relevant outcomes. In the future, algorithmic clustering by pain distribution may be an important facet in chronic pain biosignatures developed for the personalization of pain management.

Comparative Effectiveness of Embedded Mental Health Services in Pain Management Clinics vs Standard Care

OBJECTIVE

Embedded behavioral medicine services are a common component of multidisciplinary chronic pain treatment programs. However, few studies have studied whether these services are associated with improved treatment outcomes.

METHODS

Using a retrospective, matched, two-cohort study design, we examined patient-reported outcomes (PROs), including Patient-Reported Outcomes Measurement Information System pain, mental health, and physical function measures, collected at every clinic visit in every patient. Changes from baseline through 12 months were compared in those receiving embedded Behavioral Medicine in addition to usual care to a Standard Care group seen in the same pain practice and weighted via propensity scoring.

RESULTS

At baseline, Behavioral Medicine patients had worse scores on most pain, mental health, and physical health measures and were more likely to be female, a member of a racial minority, and have lower socioeconomic status. Regardless of having a worse clinical pain syndrome at baseline, at follow-up both Behavioral Medicine (N = 451) and Standard Care patients (N = 8,383) showed significant and comparable improvements in pain intensity, physical function, depression, and sleep disturbance. Behavioral Medicine patients showed significantly greater improvements in their global impressions of change than the Standard Care patients.

CONCLUSIONS

Despite worse pain and physical and psychological functioning at baseline, Behavioral Medicine patients showed improvements comparable to patients not receiving these services. Further, Behavioral Medicine patients report higher global impressions of change, indicating that embedded mental health services appear to have the additive value of amplifying the benefits of multimodal pain care.

Identifying Predictors of Recommendations for and Participation in Multimodal Nonpharmacological Treatments for Chronic Pain Using Patient-Reported Outcomes and Electronic Medical Records

OBJECTIVE

High-quality chronic pain care emphasizes multimodal treatments that include medication and nonpharmacological treatments. But it is not clear which patients will participate in nonpharmacological treatments, such as physical therapy or mental health care, and previous research has shown conflicting evidence.

METHODS

We used the Patient Outcomes Repository for Treatment (PORT) registry, which combines patient-reported outcomes data with electronic medical records. In this retrospective observational study, we performed two separate multinomial regression analyses with feature selection to identify PORT variables that were predictive of 1) recommendation of a nonpharmacological treatment by the provider and 2) patient participation in nonpharmacological treatments. Two hundred thirty-six patients were recommended (REC) or not recommended (NO REC) a nonpharmacological treatment, and all REC patients were classified as participating (YES) or not participating (NO) in the recommendations.

RESULTS

Female gender and a diagnosis of Z79 "Opioid drug therapy" were significant positive and negative predictors of nonpharmacological treatment recommendations, respectively. Schedule II opioid use at initial presentation and recommendations for rehabilitation therapy were significant predictors of nonparticipation.

CONCLUSIONS

Patients using opioids are less likely to be recommended nonpharmacological treatments as part of multimodal chronic pain care and are less likely to participate in nonpharmacological treatments once recommended. Males are also less likely to be recommended nonpharmacological treatments. Patients referred for rehabilitation therapies are less likely to comply with those recommendations. We have identified patients in vulnerable subgroups who may require additional resources and/or encouragement to comply with multimodal chronic pain treatment recommendations.

Using formative evaluation methods to improve clinical implementation efforts: Description and an example

Formative evaluation, a rigorous assessment process to identify potential and actual influences on the implementation process, is a necessary first step prior to launching any implementation effort. Without formative evaluation, intervention studies may fail to translate into meaningful patient care or public health outcomes or across different contexts. Formative evaluation usually consists of qualitative methods, but may involve quantitative or mixed methods. A unique aspect of formative evaluation is that data are shared with the implementation team during the study in order to adapt and improve the process of implementation during the course of the study or improvement activity. In implementation science, and specifically within formative evaluation, it is imperative that a theory or conceptual model or framework guide the selection of the various individual, organizational or contextual factors to be assessed. Data from these theory-based constructs can translate into the development and specification of implementation strategies to support the uptake of the intervention. In this article, we describe different types of formative evaluations (developmental, implementation-focused, progress-focused, and interpretive), and then present a formative evaluation case study from a real-world implementation study within several academic pain clinics, guided by the Theory of Diffusion of Innovation.

Chronic pain clinic efficiency analysis: optimization through use of the Gantt diagram and visit diagnoses

Objective

The aim of this study is to identify scheduling inefficiencies and to develop a personalized schedule based on diagnosis, service time (face-to-face time between the patient and the provider), and patient wait time using a Gantt diagram in a chronic pain clinic.

Design

This is an observational prospective cohort quality improvement (QI) study.

Setting

This study was carried out at a single outpatient multidisciplinary pain management clinic in a university teaching hospital.

Subjects

New and established chronic pain patients at the University of Pittsburgh Medical Center (UPMC) Montefiore Chronic Pain Clinic were recruited for this study.

Methods

Time tracking data for each phase of clinic visit and pain-related diagnoses were collected from 81 patients on 5 clinic days in March 2016 for patient flow analysis.

Results

A Gantt diagram was created using Microsoft Excel^® software. Areas of overbooking and underbooking were identified. Median service times (minutes) differed dramatically based on the diagnosis and were highest for facial pain (23 [IQR, 15–31]) and chronic abdominal and/or pelvic pain (21.5 [IQR, 16–27]) and lowest for myalgia. Abdominal and/or pelvic pain and facial pain median service times consistently exceeded the 15-minute allocation for return visits.

Conclusion

Schedule efficiency analysis using the Gantt diagram identified trends of overbooking and underbooking and inefficiencies in examination room utilization. A 15-minute appointment for all return patients is unrealistic due to variation of service times for some diagnoses. Scheduling appointments based on the diagnosis is an innovative approach that may reduce scheduling inefficiencies and improve patient satisfaction and the overall quality of care. To the best of our knowledge, this type of scheduling diagram has not been used in a chronic pain clinic.