Chronic pain is a pervasive condition that is complicated by economic, educational, and racial disparities. This study analyzes key factors associated with chronic pain within an understudied and underserved population. The sample is characterized by a triple disparity with respect to income, education/literacy, and racial barriers that substantially increase the vulnerability to the negative consequences of chronic pain. The study examined the pretreatment data of 290 participants enrolled in the Learning About My Pain trial, a randomized controlled comparative effectiveness trial of psychosocial interventions (B.E.T., Principal Investigator, Patient-Centered Outcomes Research Institute Contract No. 941; clinicaltrials.gov identifier NCT01967342) for chronic pain. Hierarchical multiple regression analyses evaluated the relationships among sociodemographic (sex, age, race, poverty status, literacy, and education level) and psychological (depressive symptoms and pain catastrophizing) variables and pain interference, pain severity, and disability. The indirect effects of depressive symptoms and pain catastrophizing on the sociodemographic and pain variables were investigated using bootstrap resampling. Reversed mediation models were also examined. Results suggested that the experience of chronic pain within this low-income sample is better accounted for by psychological factors than sex, age, race, poverty status, literacy, and education level. Depressive symptoms and pain catastrophizing mediated the relationships between age and pain variables, whereas pain catastrophizing mediated the effects of primary literacy and poverty status. Some reversed models were equivalent to the hypothesized models, suggesting the possibility of bidirectionality. Although cross-sectional findings cannot establish causality, our results highlight the critical role psychological factors play in individuals with chronic pain and multiple health disparities.

Background Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. Trial Registration ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642

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significantly greater reductions in pain severity, F (1, 73) = 6.57, p = .006, d =-0.67, pain interference (p = .002, d =-0.75), global psychological symptoms (p = .01, d =-0.55), and specifically reduced depression (p = .03, d =-0.51) and interpersonal sensitivity (p = .02, d =-0.57) at follow-up, compared to controls. Clinical observations and patient reports indicated that the vast majority of patients had substantial unresolved victimization, conflict, and/or secrets, which typically had not been disclosed in this setting. Even though patients struggled to express their emotions, they were typically thankful for this interview opportunity. This study demonstrates the need for and value of interventions that target emotional disclosure and expression for pain disorders in primary care. (489) An examination of the roles of perceived injustice and pain acceptance on pain interference and pain intensity in patients with chronic pain: A Collaborative Health Outcomes Information Registry (CHOIR) Study

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The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in the treatment of adults with major depression. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. For both physicians and patients, study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust in the CDSS. 17 patients consented to participate in the study, of which 14 completed. 86% of physicians (6/7) felt the information provided by the CDSS provided a more comprehensive understanding of patient situations and 71% (5/7) felt the information was helpful. 86% of physicians (6/7) reported the AI/predictive model was useful when making treatment decisions. 62% of patients (8/13) reported improvement in their care as a result of the tool. 46% of patients (6/13) felt the app significantly or somewhat improved their relationship with their physicians; 54% felt it did not change. 71% of physicians (5/7) and 62% of patients (8/13) rated they trusted the tool. Qualitative results are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while making treatment decisions. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships.