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ImportanceThe National Academy of Medicine's National Plan for Health Workforce Well-Being provides recommendations for supporting the mental health and well-being of health care workers. This article aims to guide implementation of National Academy of Medicine recommendations by describing 2 programs at Columbia University Irving Medical Center and the University of California, San Francisco (UCSF), designed early in the COVID-19 pandemic to respond to the behavioral health needs of the health care workforce. The development of these programs, their similarities and differences, and the key lessons learned are discussed.ObservationsThe well-being programs, CopeColumbia and UCSF Cope, shared key elements. Both efforts were led by their respective departments of psychiatry and used similar frameworks. Teams created strategic cross-university partnerships to share difficulties and successes across both programs. Moreover, both programs addressed compounding stressors of racial and political unrest, evaluated program components, and created resources for employee self-management. CopeColumbia and UCSF Cope differed in approaches to identifying high-risk employees and formal assessment and treatment pathways. From the authors' experience implementing these programs and having knowledge regarding health care workforce burnout, this article offers recommendations for the development of well-being programs. These include structural changes and resources to promote group and individual well-being emphasizing equity and justice, intentional involvement of psychiatry on well-being leadership teams, and bold efforts to destigmatize mental health care alongside clear paths to mental health treatment.Conclusions and relevanceThe impact of the COVID-19 pandemic revealed a need for institutions to support the mental health and emotional well-being of health care workers. By outlining the development and implementation of 2 well-being programs in large academic health care settings and making recommendations to promote workforce well-being, it is the authors' hope that leaders will be empowered to carry forward critical changes. Most importantly, implementing plans now will provide the resilience needed both for the long shadow of the pandemic and future crises.

BackgroundDuring the COVID-19 pandemic, health care systems were faced with the urgent need to implement strategies to address the behavioral health needs of health care workers. A primary concern of any large health care system is developing an easy-to-access, streamlined system of triage and support despite limited behavioral health resources.ObjectiveThis study provides a detailed description of the design and implementation of a chatbot program designed to triage and facilitate access to behavioral health assessment and treatment for the workforce of a large academic medical center. The University of California, San Francisco (UCSF) Faculty, Staff, and Trainee Coping and Resiliency Program (UCSF Cope) aimed to provide timely access to a live telehealth navigator for triage and live telehealth assessment and treatment, curated web-based self-management tools, and nontreatment support groups for those experiencing stress related to their unique roles.MethodsIn a public-private partnership, the UCSF Cope team built a chatbot to triage employees based on behavioral health needs. The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. The goal of each chatbot session was to guide users to services that were appropriate for their needs. Designers developed a chatbot data dashboard to identify and follow trends directly through the chatbot. Regarding other program elements, website user data were collected monthly and participant satisfaction was gathered for each nontreatment support group.ResultsThe UCSF Cope chatbot was rapidly developed and launched on April 20, 2020. As of May 31, 2022, a total of 10.88% (3785/34,790) of employees accessed the technology. Among those reporting any form of psychological distress, 39.7% (708/1783) of employees requested in-person services, including those who had an existing provider. UCSF employees responded positively to all program elements. As of May 31, 2022, the UCSF Cope website had 615,334 unique users, with 66,585 unique views of webinars and 601,471 unique views of video shorts. All units across UCSF were reached by UCSF Cope staff for special interventions, with >40 units requesting these services. Town halls were particularly well received, with >80% of attendees reporting the experience as helpful.ConclusionsUCSF Cope used chatbot technology to incorporate individualized behavioral health triage, assessment, treatment, and general emotional support for an entire employee base (N=34,790). This level of triage for a population of this size would not have been possible without the use of chatbot technology. The UCSF Cope model has the potential to be scaled, adapted, and implemented across both academically and nonacademically affiliated medical settings.

To present the structure and outcomes to date for the Psychiatric Assessment and Brief Intervention (PABI), a pilot program developed at University of California, San Francisco, to improve access of primary care patients to mental health services. PABI offers diagnostic evaluations and brief (up to 3 months) evidence-based treatment, including pharmacologic management and psychotherapy, to medical patients 18 years of age and older. Core PABI features are ensuring prompt access, actively partnering with patients and referring providers, and coordinating seamless transitions of care.Demographic and clinical variables and outcome indicators were collected for all patients seen in PABI from October 2015 to June 2017. Descriptive statistics and mixed-effects linear models were used to analyze the data.During the study period, 139 patients (54% women, mean [SD] age of 48.2 [17.5] years) with a mean of 2 DSM-5 psychiatric diagnoses each (range, 1-5) were seen. Mean time to access was 8 days, with a mean length of stay in the program of 11 weeks. Compared to baseline, final behavioral health measure scores showed significant improvement: the mean Patient Health Questionnaire-9 score decreased by 5.9 points (95% CI, 4.6-7.2), and the mean 7-item Generalized Anxiety Disorder scale score was 4.4 points lower (95% CI, 3.2-5.6; both P values < .0001).This brief psychiatric treatment program provides prompt access to quality mental health care for patients with medical comorbidities. Results to date suggest that this program leads to significantly improved clinical outcomes. Further research is needed to determine its long-term sustainability and generalizability.

ImportanceEncouraging primary care patients to address depression symptoms and care with clinicians could improve outcomes but may also result in unnecessary treatment.ObjectiveTo determine whether a depression engagement video (DEV) or a tailored interactive multimedia computer program (IMCP) improves initial depression care compared with a control without increasing unnecessary antidepressant prescribing.Design, setting, and participantsRandomized clinical trial comparing DEV, IMCP, and control among 925 adult patients treated by 135 primary care clinicians (603 patients with depression and 322 patients without depression, defined by Patient Health Questionnaire-9 [PHQ-9] score) conducted from June 2010 through March 2012 at 7 primary care clinical sites in California.InterventionsDEV targeted to sex and income, an IMCP tailored to individual patient characteristics, and a sleep hygiene video (control).Main outcomes and measuresAmong depressed patients, superiority assessment of the composite measure of patient-reported antidepressant drug recommendation, mental health referral, or both (primary outcome); depression at 12-week follow-up, measured by the PHQ-8 (secondary outcome). Among nondepressed patients, noninferiority assessment of clinician- and patient-reported antidepressant drug recommendation (primary outcomes) with a noninferiority margin of 3.5%. Analyses were cluster adjusted.ResultsOf the 925 eligible patients, 867 were included in the primary analysis (depressed, 559; nondepressed, 308). Among depressed patients, rates of achieving the primary outcome were 17.5% for DEV, 26% for IMCP, and 16.3% for control (DEV vs control, 1.1 [95% CI, -6.7 to 8.9], P = .79; IMCP vs control, 9.9 [95% CI, 1.6 to 18.2], P = .02). There were no effects on PHQ-8 measured depression score at the 12-week follow-up: DEV vs control, -0.2 (95% CI, -1.2 to 0.8); IMCP vs control,  0.9 (95% CI, -0.1 to 1.9). Among nondepressed patients, clinician-reported antidepressant prescribing in the DEV and IMCP groups was noninferior to control (mean percentage point difference [PPD]: DEV vs control, -2.2 [90% CI, -8.0 to 3.49], P = .0499 for noninferiority; IMCP vs control, -3.3 [90% CI, -9.1 to 2.4], P = .02 for noninferiority); patient-reported antidepressant recommendation did not achieve noninferiority (mean PPD: DEV vs control,  0.9 [90% CI, -4.9 to 6.7], P = .23 for noninferiority; IMCP vs control,  0.3 [90% CI, -5.1 to 5.7], P = .16 for noninferiority).Conclusions and relevanceA tailored IMCP increased clinician recommendations for antidepressant drugs, a mental health referral, or both among depressed patients but had no effect on mental health at the 12-week follow-up. The possibility that the IMCP and DEV increased patient-reported clinician recommendations for an antidepressant drug among nondepressed patients could not be excluded.Trial registrationclinicaltrials.gov Identifier: NCT01144104.

Background: During the COVID-19 pandemic, health care systems were faced with the urgent need to implement strategies to address the behavioral health needs of health care workers. A primary concern of any large health care system is developing an easy-to-access, streamlined system of triage and support despite limited behavioral health resources., Objective: This study provides a detailed description of the design and implementation of a chatbot program designed to triage and facilitate access to behavioral health assessment and treatment for the workforce of a large academic medical center. The University of California, San Francisco (UCSF) Faculty, Staff, and Trainee Coping and Resiliency Program (UCSF Cope) aimed to provide timely access to a live telehealth navigator for triage and live telehealth assessment and treatment, curated web-based self-management tools, and nontreatment support groups for those experiencing stress related to their unique roles., Methods: In a public-private partnership, the UCSF Cope team built a chatbot to triage employees based on behavioral health needs. The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. The goal of each chatbot session was to guide users to services that were appropriate for their needs. Designers developed a chatbot data dashboard to identify and follow trends directly through the chatbot. Regarding other program elements, website user data were collected monthly and participant satisfaction was gathered for each nontreatment support group., Results: The UCSF Cope chatbot was rapidly developed and launched on April 20, 2020. As of May 31, 2022, a total of 10.88% (3785/34,790) of employees accessed the technology. Among those reporting any form of psychological distress, 39.7% (708/1783) of employees requested in-person services, including those who had an existing provider. UCSF employees responded positively to all program elements. As of May 31, 2022, the UCSF Cope website had 615,334 unique users, with 66,585 unique views of webinars and 601,471 unique views of video shorts. All units across UCSF were reached by UCSF Cope staff for special interventions, with >40 units requesting these services. Town halls were particularly well received, with >80% of attendees reporting the experience as helpful., Conclusions: UCSF Cope used chatbot technology to incorporate individualized behavioral health triage, assessment, treatment, and general emotional support for an entire employee base (N=34,790). This level of triage for a population of this size would not have been possible without the use of chatbot technology. The UCSF Cope model has the potential to be scaled, adapted, and implemented across both academically and nonacademically affiliated medical settings., (©Maga Jackson-Triche, Don Vetal, Eva-Marie Turner, Priya Dahiya, Christina Mangurian. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.06.2023.)