Implementing a resilience coach program to support first year housestaff during the COVID-19 pandemic: early pilot results and comparison with non-housestaff sessions.

BACKGROUND: In response to the COVID-19 pandemic, we launched the Penn Medicine Coping First Aid program to provide psychosocial supports to our health system community. Our approach leveraged lay health worker volunteers trained in principles of Psychological First Aid to deliver coaching services through a centralized virtual platform. METHODS: We emailed all (n = 408) first year housestaff (i.e., residents and fellows) with an invitation to schedule a session with a resilience coach. We compared the mental health concerns, symptoms, and Psychological First Aid techniques recorded in (n = 67) first year housestaff sessions with (n = 91) sessions of other employees in the health system. RESULTS: Between June and November 2020, forty-six first year housestaff attended at least one resilience coaching session. First year housestaff most commonly presented with feelings of anxiety and sadness and shared concerns related to the availability of social support. Resilience coaches most frequently provided practical assistance and ensured safety and comfort to first year housestaff. First year housestaff reported fewer physical or mental health symptoms and held shorter sessions with resilience coaches than non-housestaff. CONCLUSIONS: This work offers insights on how to address psychosocial functioning through low-intensity interventions delivered by lay personnel. More research is needed to understand the efficacy of this program and how best to engage housestaff in wellness and resilience programs throughout training, both during and beyond COVID-19.

Improving Payment for Collaborative Mental Health Care in Primary Care.

BACKGROUND: There is strong evidence supporting implementation of the Collaborative Care Model within primary care. Fee-for-service payment codes, published by Current Procedural Terminology in 2018, have made collaborative care separately reimbursable for the first time. These codes (ie, 99492-99494) reimburse for time spent per month by any member of the care team engaged in Collaborative Care, including behavioral care managers, primary care providers, and consulting psychiatrists. Time-based billing for these codes presents challenges for providers delivering Collaborative Care services. OBJECTIVES: Based on experience from multiple health care organizations, we reflect on these challenges and provide suggestions for implementation and future refinement of the codes. CONCLUSIONS: Further refinements to the codes are encouraged, including moving from a calendar month to a 30-day reimbursement cycle. In addition, we recommend payers adopt the new code proposed by the Centers for Medicare and Medicaid Services to account for smaller increments of time.

Addressing Common Challenges in the Implementation of Collaborative Care for Mental Health: The Penn Integrated Care Program.

PURPOSE: We developed and implemented a new model of collaborative care that includes a triage and referral management system. We present initial implementation metrics using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. METHODS: Primary care clinicians in 8 practices referred patients with any unmet mental health needs to the Penn Integrated Care program. Assessments were conducted using validated measures. Patients were primarily triaged to collaborative care (26%) or specialty mental health care with active referral management (70%). We conducted 50 qualitative interviews to understand the implementation process and inform program refinement. Our primary outcomes were reach and implementation metrics, including referral and encounter rates derived from the electronic health record. RESULTS: In 12 months, 6,124 unique patients were referred. Assessed patients reported symptoms consistent with a range of conditions from mild to moderate depression and anxiety to serious mental illnesses including psychosis and acute suicidal ideation. Among patients enrolled in collaborative care, treatment entailed a mean of 7.2 (SD 5.1) encounters over 78.1 (SD 51.3) days. Remission of symptoms was achieved by 32.6% of patients with depression and 39.5% of patients with anxiety. Stakeholders viewed the program favorably and had concrete suggestions to ensure sustainability. CONCLUSIONS: The Penn Integrated Care program demonstrated broad reach. Implementation was consistent with collaborative care as delivered in seminal studies of the model. Our results provide insight into a model for launching and implementing collaborative care to meet the needs of a diverse group of patients with the full range of mental health conditions seen in primary care.

A pilot study of participatory and rapid implementation approaches to increase depression screening in primary care.

BACKGROUND: Most individuals with depression go unidentified and untreated. In 2016 the US Preventive Services Task Force released guidelines recommending universal screening in primary care to identify patients with depression and to link them to treatment. Feasible, acceptable, and effective strategies to implement these guidelines are needed. METHODS: This three-phased study employed rapid participatory methods to design and test strategies to increase depression screening at Penn Medicine, a large health system with 90 primary care practices. First, researchers solicited ideas and barriers from stakeholders to increase screening using an innovation tournament-a crowdsourcing method that invites stakeholders to submit ideas to address a workplace challenge. Second, a panel of stakeholders and scientists deliberated over and ranked the tournament ideas. An instant runoff election was held to select the winning idea. Third, the research team piloted the winning idea in a primary care practice using rapid prototyping, an approach that quickly refines and iterates strategy designs. RESULTS: The innovation tournament yielded 31 ideas and 32 barriers from diverse stakeholders (12 primary care physicians, 10 medical assistants, 4 nurse practitioners, 2 practice managers, and 4 patient support assistants). A panel of 6 stakeholders and scientists deliberated on the ideas and voted for patient self-report (i.e., through tablet computers, text message, or an online patient portal) as the winning idea. The research team rapid prototyped tablets in one primary care practice with one physician over 5 five-hour shifts to examine the feasibility, acceptability, and effectiveness of the strategy. Most patients, the physician, and medical assistants found the tablets acceptable and feasible. However, patient support assistants struggled to incorporate them in their workflow and expressed concerns about scaling up the process. Depression screening rates were higher using tablets compared to usual care; follow-up was comparable between tablets and usual care. CONCLUSIONS: Rapid participatory methods engaged and amplified the voices of diverse stakeholders in primary care. These methods helped design an acceptable and feasible implementation strategy that showed promise for increasing depression screening in a primary care setting. The next step is to evaluate the strategy in a randomized controlled trial across primary care practices.

Cross-sectional analysis of healthcare worker mental health and utilisation of a digital mental health platform from 2020 to 2023.

BACKGROUND: Healthcare worker (HCW) anxiety and depression worsened during the pandemic, prompting the expansion of digital mental health platforms as potential solutions offering online assessments, access to resources and counselling. The use of these digital engagement tools may reflect tendencies and trends for the mental health needs of HCWs. OBJECTIVES: This retrospective, cross-sectional study investigated the association between the use of an online mental health platform within a large academic health system and measures of that system's COVID-19 burden during the first 3 years of the pandemic. METHODS: The study investigated the use of Cobalt, an online mental health platform, comprising deidentified mental health assessments and utilisation metrics. Cobalt, serves as an online mental health resource broadly available to health system employees, offering online evidence-based tools, coaching, therapy options and asynchronous content (podcasts, articles, videos and more). The analyses use validated mental health assessments (Generalised Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and post-traumatic stress disorder (PTSD)) alongside publicly available COVID-19 data. Statistical analyses employed univariate linear regression with Stata SE Statistical Software. RESULTS: Between March 2020 and March 2023, 43 308 independent user sessions were created on Cobalt, a majority being anonymous sessions (72%, n=31 151). Mental health assessments, including PHQ-4, PHQ-9, GAD-7 and primary care-PTSD, totalled 9462 over the time period. Risk for self-harm was noted in 17.1% of PHQ-9 assessments. Additionally, 4418 appointments were scheduled with mental health counsellors and clinicians. No significant associations were identified between COVID-19 case burden and Cobalt utilisation or assessment scores. CONCLUSION: Cobalt emerged as an important access point for assessing the collective mental health of the workforce, witnessing increased engagement over time. Notably, the study indicates the nuanced nature of HCW assessments of anxiety, depression and PTSD, with mental health scores reflecting moderate decreases in depression and anxiety but signalling potential increases in PTSD. Tailored resources are imperative, acknowledging varied mental health needs within the healthcare workforce. Ultimately, this investigation lays the groundwork for continued exploration of the impact and effectiveness of digital platforms in supporting HCW mental health.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.

BACKGROUND: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE: We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS: Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS: Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS: Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.

Impact of the collaborative care model on medical spending.

OBJECTIVES: The collaborative care model integrates mental health care into primary care. In 2017, CMS created new billing codes to reimburse collaborative care. We measured the impact of a program supported by these codes on medical spending. STUDY DESIGN: Quasi-experimental. METHODS: We identified a commercially insured and managed Medicare sample of 825 patients who received collaborative care services in 8 primary care practices. We used propensity score matching to match treated patients to potential controls, resulting in 569 patients per group. We performed a difference-in-differences regression analysis to evaluate the impact of collaborative care on total medical spending, including medical, psychiatric, and pharmaceutical claims. RESULTS: Collaborative care patients' mean total medical cost began to fall after a patient's third month in the program and fell below the mean cost of control patients at month 7. Difference-in-differences regressions indicate a nonsignificant savings in total medical cost of $29.35 per member per month for patients in collaborative care compared with matched controls (95% CI, -$226.52 to $167.82). Treated members incurred $34.11 (95% CI, $31.95-$36.27) higher primary care costs that were directly attributed to collaborative care, $19.91 (95% CI, $4.84-$34.98) higher costs for other mental or behavioral health care, and a nonsignificant reduction of $91.34 (95% CI, -$319.32 to $136.63) in inpatient costs. CONCLUSIONS: Modest spending on collaborative care services to address the behavioral health needs of patients did not increase overall health care costs. This is the first economic study of a collaborative care program supported by the new billing codes.

A call for open data to develop mental health digital biomarkers.

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.

Increasing access to mental health supports for healthcare workers during the COVID-19 pandemic and beyond through a novel coaching program.

The COVID-19 pandemic led to heightened anxiety, distress, and burnout among healthcare workers and faculty in academic medicine. Penn Medicine launched Coping First Aid (CFA) in March 2020 in response to the pandemic. Informed by Psychological First Aid principles and therapeutic micro skills, CFA was designed as a tele-mental healthcare service for health system employees and their families delivered by trained lay volunteer coaches under the supervision of licensed mental health clinicians. We present an overview of the model, feasibility and utilization data, and preliminary implementation and effectiveness outcomes based on cross sectional coach (n = 22) and client (n = 57) self-report surveys with a subset of program users in the first year. A total of 44 individuals completed training and were certified to coach. Over the first 24 months of the program, 513 sessions occurred with 273 clients (119 sessions were no-shows or canceled). Follow-up appointments were recommended in 52.6% (n = 270) of sessions and 21.2% (n = 109) of clients were referred for professional mental health care. Client survey respondents reported CFA was helpful; 60% were very or extremely satisfied, and 74% indicated they would recommend the program. Our preliminary findings suggest that CFA was feasible to implement and most clients found the service beneficial. CFA provides a model for rapidly developing and scaling mental health supports during and beyond the pandemic.

COBALT: Supporting the mental well-being of the health care workforce with technology-facilitated care during Covid-19 and beyond.

Two-thirds of health professionals facing the clinical demands of responding to the Covid-19 pandemic experience psychiatric symptoms, including post-traumatic stress, anxiety, substance use, depression, insomnia, and suicide.1,2 Compounding matters, access to mental health services is poor, quality is variable, and stigma is prevalent. COBALT, a digital mental health and wellness platform developed at Penn Medicine, was designed to support health care workers, offering a combination of self-directed resources, virtual group sessions, and individual appointments with a stepped care model of providers, including peers, resilience coaches, psychotherapists, and psychiatrists. In COBALT's first 11 months, the platform saw approximately 10,000 users, 200,000 page views, 1,400 one-on-one appointment bookings, over 1,000 group appointment reservations, and 158 interceptions of employees contemplating self-harm. COBALT reveals the unmet demand for mental health support among health professionals and provides a model for both expanding the supply of and streamlining access to services.

Collaborative care in the treatment of opioid use disorder and mental health conditions in primary care: A clinical study protocol.

BACKGROUND: People with opioid use disorder (OUD) often have a co-occurring psychiatric disorder, which elevates the risk of morbidity and mortality. Promising evidence supports the use of collaborative care for treating people with OUD in primary care. Whether collaborative care interventions that treat both OUD and psychiatric disorders will result in better outcomes is presently unknown. METHODS: The Whole Health Study is a 3-arm randomized controlled trial designed to test collaborative care treatment for OUD and the psychiatric disorders that commonly accompany OUD. Approximately 1200 primary care patients aged ≥18 years with OUD and depression, anxiety, or PTSD will be randomized to one of three conditions: (1) Augmented Usual Care, which consists of a primary care physician (PCP) waivered to prescribe buprenorphine and an addiction psychiatrist to consult on medication-assisted treatment; (2) Collaborative Care, which consists of a waivered PCP, a mental health care manager trained in psychosocial treatments for OUD and psychiatric disorders, and an addiction psychiatrist who provides consultation for OUD and mental health; or (3) Collaborative Care Plus, which consists of all the elements of the Collaborative Care arm plus a Certified Recovery Specialist to help with treatment engagement and retention. Primary outcomes are six-month rates of opioid use and six-month rates of remission of co-occurring psychiatric disorders. DISCUSSION: The Whole Health Study will investigate whether collaborative care models that address OUD and co-occurring depression, anxiety, or PTSD will result in better patient outcomes. The results will inform clinical care delivery during the current opioid crisis. CLINICAL TRIALS REGISTRATION: www.clinicaltrials.gov registration: NCT04245423.

Involving Parents/Family in Treatment during the Transition from Late Adolescence to Young Adulthood: Rationale, Strategies, Ethics, and Legal Issues.

The progression from adolescence to adulthood is a time of tremendous change, characterized by issues of identity formation, autonomy, and shifting relationship dynamics. The family is embedded in all aspects of this transition and serves as both a protective support and a limiting factor, a complicated duality that raises psychological, ethical, and legal issues. This article discusses the influence of familial factors and provides assessment strategies for evaluating the family in relation to treatment of transitional age youth. It is increasingly evident that family engagement is a significant contributor to outcomes for transitional age youth seeking mental health treatment.