Literature Collection

BACKGROUND: Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. OBJECTIVES: We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. METHODS: Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated. RESULTS: The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%). CONCLUSIONS: Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

Background: The opioid epidemic continues to erode communities across Pennsylvania (PA). Federal and PA state programs developed grants to establish Hub and Spoke programs for the expansion of medications for opioid use disorders (MOUD). Employing the telementoring platform Project ECHO (Extension for Community Health Outcomes), Penn State Health engaged the other seven grant awardees in a Collaborative Health Systems (CHS) ECHO. We conducted key informant interviews to better understand impact of the CHS ECHO on health systems collaboration and opioid crisis efforts. Methods: For eight one-hour sessions, each awardee presented their unique strategies, challenges, and opportunities. Using REDCap, program characteristics, such as number of waivered prescribers and number of patients served were collected at baseline. After completion of the sessions, key informant interviews were conducted to assess the impact of CHS ECHO on awardee's programs. Results: Analysis of key informant interviews revealed important themes to address opioid crisis efforts, including the need for strategic and proactive program reevaluation and the convenience of collaborative peer learning networks. Participants expressed benefits of the CHS ECHO including allowing space for discussion of challenges and best practices and facilitating conversation on collaborative targeted advocacy and systems-level improvements. Participants further reported bolstered motivation and confidence. Conclusions: Utilizing Project ECHO provided a bidirectional platform of learning and support that created important connections between institutions working to combat the opioid epidemic. CHS ECHO was a unique opportunity for productive and convenient peer learning across external partners. Open dialogue developed during CHS ECHO can continue to direct systems-levels improvements that benefit individual and population outcomes.

PURPOSE: Little is known about the use of collaborative care models for patients with co-occurring chronic pain and substance use disorders (SUD). This study aimed to pilot test a collaborative care intervention delivered over telehealth to rural patients engaged with SUD treatment who experienced chronic pain. DESIGN: Single-arm, open-label pilot intervention trial. METHODS: Patients (N=88) were enrolled in SUD treatment at a single VA Medical Center and endorsed moderate-to-severe chronic pain. Patients received a nurse-led collaborative care intervention consisting of a comprehensive pain assessment, up to six follow-up appointments with the nurse care manager (NCM), and an optional 10-session pain education class. All patient encounters occurred remotely via telehealth. Baseline, 1- and 4-month follow up assessments measured outcomes of pain, depression, and substance use. Generalized estimating equations and intent-to-treat procedures modelled changes in outcomes over time. RESULTS: Patients were predominantly male (85%) and white (85%), with high mental health and substance use disorder comorbidities (92%). The most common substances of use at treatment initiation were alcohol (49%), opioids (17%), cannabis (17%), methamphetamine (11%), and cocaine (6%). By 4-month follow-up, patients who received the pain intervention endorsed significant reductions in pain intensity, pain interference, and depressive symptoms. Among patients using alcohol or cannabis at baseline, significant reductions in days using these substances were also observed. CONCLUSIONS AND CLINICAL IMPLICATIONS: An NCM-led collaborative care intervention delivered via telehealth may improve both pain and substance use outcomes for rural patients with these comorbidities. Large-scale clinical trials are needed to demonstrate intervention efficacy.

Opioid use is a major problem in India and has high morbidity and mortality with a prevalence of 2.06%. There is a huge treatment gap for opioid use disorders (OUDs). Due to limited mental health resources and limited psychiatric training of medical practitioners in OUDs, a significant proportion of patients do not receive appropriate medical intervention. This article demonstrates how a primary care doctor working in a remote opioid substitution therapy (OST) clinic received assistance from the optional opioid module of clinical schedule for primary care psychiatry (CSP) and collaborative video consultation (CVC) module to address specific difficulties of patients already on Buprenorphine OST and improve the quality of care, thereby reducing chances of relapses. CVC module is a part of one-year digitally driven primary care psychiatry program designed by National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru. The opioid module was designed by NIMHANS, Bengaluru in collaboration with the All India Institute of Medical Sciences (AIIMS), New Delhi These observations warrant replication of this approach across diverse settings and at a larger scale to explore and evaluate its impact and effectiveness.

OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns. DATA SOURCES: United States Veterans Health Administration Corporate Data Warehouse. STUDY DESIGN: For Operation Enduring Freedom/Operation Iraqi Freedom veterans with major depressive disorder, we generate pharmacotherapy pathways (of antidepressants) using process mining and machine learning. We select the medication episodes that were started at subtherapeutic doses by the first assigned primary care physician and observe the paths that those medication episodes follow. Using 2-stage least squares, we test the effectiveness of starting at a low dose and staying low for longer versus ramping up fast while balancing observable and unobservable characteristics of patients and providers through instrumental variables. We leverage predetermined provider practice patterns as instruments. DATA COLLECTION: We collected outpatient pharmacy data for selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors, patient and provider characteristics (as control variables), and the instruments for our cohort. All data were extracted for the period between 2006 and 2020. PRINCIPAL FINDINGS: There is a statistically significant positive effect (0.68, 95% CI 0.11-1.25) of "ramping up fast" on engagement in care. When we examine the effect of "ramping up slow", we see an insignificant negative impact on engagement in care (-0.82, 95% CI -1.89 to 0.25). As expected, the probability of drop-out also seems to have a negative effect on engagement in care (-0.39, 95% CI -0.94 to 0.17). We further validate these results by testing with medication possession ratios calculated periodically as an alternative engagement in care metric. CONCLUSIONS: Our findings contradict the "Start low, go slow" adage, indicating that ramping up the dose of an antidepressant faster has a significantly positive effect on engagement in care for our population.

BACKGROUND: Telemedicine-delivered buprenorphine (tele-buprenorphine) can potentially increase access to buprenorphine for patients with opioid use disorder (OUD), especially during the COVID-19 pandemic, but we know little about use in clinical care. METHODS: This study was a retrospective national cohort study of veterans diagnosed with opioid use disorder (OUD) receiving buprenorphine treatment from the Veterans Health Administration (VHA) in fiscal years 2012-2019. The study examined trends in use of tele-buprenorphine and compared demographic and clinical characteristics in patients who received tele-buprenorphine versus those who received in-person treatment only. RESULTS: Utilization of tele-buprenorphine increased from 2.29% of buprenorphine patients in FY2012 (n = 187) to 7.96% (n = 1352) in FY2019 in VHA veterans nationally. Compared to patients receiving only in-person care, tele-buprenorphine patients were less likely to be male (AOR = 0.85, 95% CI: 0.73-0.98) or Black (AOR = 0.54, 95% CI: 0.45-0.65). Tele-buprenorphine patients were more likely to be treated in community-based outpatient clinics rather than large medical centers (AOR = 2.91, 95% CI: 2.67-3.17) and to live in rural areas (AOR = 2.12, 95% CI:1.92-2.35). The median days supplied of buprenorphine treatment was 722 (interquartile range: 322-1459) among the tele-buprenorphine patients compared to 295 (interquartile range: 67-854) among patients who received treatment in-person. CONCLUSIONS: Use of telemedicine to deliver buprenorphine treatment in VHA increased 3.5-fold between 2012 and 2019, though overall use remained low prior to COVID-19. Tele-buprenorphine is a promising modality especially when treatment access is limited. However, we must continue to understand how practitioners and patient are using telemedicine and how these patients' outcomes compare to those using in-person care.