Transitioning Virtual-Only Group Therapy for Substance Use Disorder Patients to a Hybrid Model.

Purpose: Telehealth is associated with a myriad of benefits; however, little is known regarding substance use disorder (SUD) treatment outcomes when participants join group therapy sessions in a combination in-person and virtual setting (hybrid model). We sought to determine if treatment completion rates differed. Patients and Methods: Policy changes caused by the COVID-19 pandemic created a naturalistic, observational cohort study at seven intensive outpatient (IOP) programs in rural Minnesota. Virtual-only delivery occurred 6/1/2020-6/30/2021, while hybrid groups occurred 7/1/2021-7/31/2022. Data was evaluated retrospectively for participants who initiated and discharged treatment during the study period. Participants were IOP group members 18 years and older who had a SUD diagnosis that both entered and discharged treatment during the 26-month period. A consecutive sample of 1502 participants (181-255 per site) was available, with 644 removed: 576 discharged after the study conclusion, 49 were missing either enrollment or discharge data, 14 transferred sites during treatment, and 5 initiated treatment before the study initiation. Helmert contrasts evaluated the impact of hybrid group exposure. Results: A total of 858 individuals were included. Data was not from the medical chart and was deidentified preventing specific demographics; however, the overall IOP sample for 2020-2022, from which the sample was derived, was 29.8% female, and 64.1% were 18-40 years of age. For completed treatment, hybrid group exposure relative to virtual-only had a univariate odds ratio of 1.88 (95% CI: 1.50-2.41, p < 0.001). No significant difference was seen across IOP sites. Conclusion: These results describe a novel hybrid group approach to virtual care for SUDs with outcome data not previously documented in the literature. While virtual treatment delivery can increase access, these results suggest a benefit is derived from including an in-person option. Further research is needed to identify how an in-person component may change dynamics and if it can be replicated in virtual-only models.

Improving Supervisor Confidence in Responding to Distressed Health Care Employees.

BACKGROUND: Professional distress and burnout are increasingly common among health professionals. This trend prompted stakeholders at a large multicenter health care system to survey supervisors for improvement opportunities. The stakeholders learned that workplace leaders lacked tools and direction for appropriately responding to distressed employees. The authors implemented a supervisor training video on providing resources to improve employee mental health. METHODS: Using the DMAIC (Define, Measure, Analyze, Improve, and Control) methodology, the authors conducted key stakeholder interviews to identify strengths, weaknesses, opportunities, and threats. Next, an e-mail survey was administered to a representative sample of supervisors that asked about degree of confidence in responding appropriately to distressed employees, with the response options "very confident," "somewhat confident," and "not at all confident." After identifying factors contributing to low supervisor confidence, the research team developed and disseminated a six-minute, on-demand video to train supervisors to respond appropriately to employees during a mental health crisis. The same group of supervisors were surveyed using the same survey after exposure to the video, and responses were collected from those who had viewed the video but had not answered the preintervention survey. RESULTS: The proportion of supervisors who responded "not at all confident" in the survey decreased from 7.1% (15/210) of responses to 0.8% (1/123), while the proportion of supervisors who chose "somewhat confident" increased significantly, from 62.9% (132/210) to 69.1% (85/123) (p = 0.03). Of the 28 supervisors who had not participated in the presurvey and viewed the video, none indicated that they were "not at all confident." The percentage of supervisors who felt distress "sometimes" or more frequently from navigating and supporting employee emotional concerns decreased nonsignificantly from 41.9% (88/210) to 37.4% (46/123) (p = 0.87). CONCLUSION: Simple, on-demand supervisor training videos can improve the confidence of supervisors to respond appropriately to distressed employees, which may indirectly contribute to improved employee mental health.

A risk identification model for detection of patients at risk of antidepressant discontinuation.

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources. Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.com," utilizing content analysis to create PsyRisk-a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms.

BACKGROUND AND OBJECTIVES: Substance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs. METHODS: We performed a systematic, comprehensive literature review and screened 1942 papers. RESULTS: We found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O2 , Accelerometer, EDA, temperature) and implemented study-specific algorithms to evaluate the data. DISCUSSION AND CONCLUSIONS: Studies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing "real-time" results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices. SCIENTIFIC SIGNIFICANCE: This is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

Substance Use Disorders and Telehealth in the COVID-19 Pandemic Era: A New Outlook.

During the current coronavirus disease 2019 epidemic, many outpatient chemical dependency treatment programs and clinics are decreasing their number of in-person patient contacts. This has widened an already large gap between patients with substance use disorders (SUDs) who need treatment and those who have actually received treatment. For a disorder where group therapy has been the mainstay treatment option for decades, social distancing, shelter in place, and treatment discontinuation have created an urgent need for alternative approaches to addiction treatment. In an attempt to continue some care for patients in need, many medical institutions have transitioned to a virtual environment to promote safe social distancing. Although there is ample evidence to support telemedical interventions, these can be difficult to implement, especially in the SUD population. This article reviews current literature for the use of telehealth interventions in the treatment of SUDs and offers recommendations on safe and effective implementation strategies based on the current literature.