ABSTRACT
BACKGROUND AND OBJECTIVES: The learning healthcare system (LHS) has been developed to integrate patients' clinical data into clinical decisions and improve treatment outcomes. Having little guidance on this integration process, we aim to explain (a) an applicable analytic tool for clinicians to evaluate the clinical outcomes at a group and an individual level and (b) our quality improvement (QI) project, analyzing the outcomes of a new outpatient pain rehabilitation program ("Back-in-Action": BIA) and applying the analysis results to modify our clinical practice. METHODS: Through our LHS (CHOIR; https://choir.stanford.edu), we administered the Pain Catastrophizing Scale (PCS), Chronic Pain Acceptance Questionnaire (CPAQ), and Patient-Reported Outcomes Measures (PROMIS)® before and after BIA. After searching for appropriate analytic tools, we decided to use the Reliable Change Index (RCI) to determine if an observed change in the direction of better (improvement) or worse (deterioration) would be beyond or within the measurement error (no change). RESULTS: Our RCI calculations revealed that at least a 9-point decrease in the PCS scores and 10-point increase in the CPAQ scores would indicate reliable improvement. RCIs for the PROMIS measures ranged from 5 to 8 T-score points (i.e., 0.5-0.8 SD). When evaluating change scores of the PCS, CPAQ, and PROMIS measures, we found that 94% of patients showed improvement in at least one domain after BIA and 6% showed no reliable improvement. CONCLUSIONS: Our QI project revealed RCI as a useful tool to evaluate treatment outcomes at a group and an individual level, and RCI could be incorporated into the LHS to generate a progress report automatically for clinicians. We further explained how clinicians could use RCI results to modify a clinical practice, to improve the outcomes of a pain program, and to develop individualized care plans. Lastly, we suggested future research areas to improve the LHS application in pain practice.
ABSTRACT
PURPOSE/OBJECTIVE: This study sought to investigate the extent to which demographic and clinical characteristics predict which patients drop out of an interdisciplinary pain management program (IPP). RESEARCH METHOD/DESIGN: Participants (N = 178 outpatients, 18-75 years of age) received treatment for various chronic pain conditions in an IPP (including biopsychosocial assessment, cognitive-behavioral, and physical therapies). Separate logistic regression analyses identified the demographic and clinical variables most predictive of attrition across five domains: (a) demographics, (b) number of medical and non/psychiatric diagnoses, (c) opioid use (yes versus no)/risk of misuse, (d) pain-related cognition and behavior, and (e) physical, social, and mental well-being. Significant predictors from the five domains were integrated in a final multivariable logistic regression model. RESULTS: Among patients exposed to a 4-week IPP, 34% dropped out. In the final model, significant predictors of higher odds of attrition included younger age or being unemployed. Also, patients on opioids at preintervention had higher odds of completing the IPP than patients not on opioids at preintervention. Follow-up analyses revealed 24 of 37 completers (65%) on opioids at preintervention reduced or eliminated use over the course of the IPP. CONCLUSIONS/IMPLICATIONS: Because findings are limited by sample and design characteristics, they require replication yet offer novel hypotheses for identifying patients at risk of attrition. Specifically, patients with preintervention opioid use (contrasted with opioid dependence) may particularly benefit from an IPP. Patients at highest risk for early dropout can be targeted for specific engagement interventions to promote completion and effectiveness of IPP. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
