What is the problem?

Building the evidence base for the efficacy of behavioral health interventions is complex. In addition to the lack of clinical trial diversity that plagues all types of therapeutics, challenges in the behavioral health space are compounded by the fact that evidence for behavioral health treatment relies on more qualitative metrics than other medical conditions. There's also less investment in measuring behavioral health compared to other facets of health. Life sciences organizations can help the industry make progress by redefining behavioral health metrics to be patient-centered and accurately measure outcomes.

What can life sciences organizations do?

1. Adopt a community-first approach to clinical trials to increase diversity and impact of clinical trials. By focusing on engaging communities rather than individual sites and addressing non-clinical barriers to participation, life sciences organizations can increase clinical trial access and diversity to build the evidence base for how behavioral health interventions work for patients of different backgrounds.

a) Identify communities you want to reach and thoughtfully design clinical trials with them at the center. Collaborative interventions between life sciences organizations and community-based mental health service providers will help life sciences organizations understand and address non-clinical barriers to clinical trial participation.

b) Standardize data-sharing forms to be compatible with major partner entities. Adopt enhanced data collection capabilities to support appropriate collection and sharing of racial and ethnic data and real-world data (RWD) through a cloud-based platform.

c) Make community organizations and patients true co-collaborators. Engage patients and community leaders in clinical trial design early and effectively leverage their input. Provide sustainable support by establishing a regular cadence to exchange data and localized insights with community partners.

2. Maximize the return on investment in RWD. Real-word data can generate useful insights into behavioral health interventions. Life sciences organizations can build a body of evidence to understand which treatments work best for patients from different backgrounds. RWD can also be used to show the long-term impact of behavioral health solutions.

a) Breakdown data silos to enable cross-functional uses of RWD. Life sciences companies are accumulating new data assets and proprietary tools that can help generate insights to fuel analytic capabilities in behavioral health. However, the clinical utility of this data is impeded due to data often being stored in separate silos under different functional or therapeutically focused teams. Developing strategic cross-functional RWD initiatives to quantify outcomes of behavioral health interventions will lay the foundation for more accurate measures of real-world efficacy in the behavioral health space.

b) Align on purpose to strengthen “fit for” standards. Aligning on purpose—how stakeholders are going to procure, disseminate, and enable people to use RWD—can help life sciences organizations maximize their data budgets by avoiding redundancies, preventing underutilization, and identifying opportunities to supplement data. Use cases for RWD are expanding: RWD can be used in research and development, in regulatory processes to inform real-world efficacy and expanded indication, and in health systems and hospitals to guide decision-making for pharmacy and therapeutics (P&T) and value analysis committees (VACs). Life sciences organizations can partner with community-based mental health service providers to design cross-functional RWD initiatives that close evidence gaps and identify patterns or disparities in adherence, utilization, and outcomes.

3. Collaborate with payers and providers to redesign metrics used to measure behavioral health outcomes. Behavioral health performance metrics are primarily concerned with quality-of-life changes, which makes it hard to precisely quantify outcomes and generate evidence for different interventions. To fully understand which treatments work for which populations, and why, the industry needs to improve how we define and measure outcomes.

a) Accurately measuring the impact of interventions requires moving from patient-reported outcomes to patient-centered outcomes. Patient-reported outcomes are based on accepted health standards that are otherwise not individualized or patient-centered. Patient-centered outcomes are created based on significant engagement from the patient and aim to optimize patient-provided information to accomplish a treatment or care goal. Measuring nontraditional metrics like quality of life, employment status, and relationship health as outcomes is important for payers to understand how to translate these outcomes into coverage decisions. This includes the creation of culturally sensitive metrics that reflect what patients prioritize and define as beneficial outcomes.

b) Design studies to analyze the ROI of non-medication behavioral health interventions when used in conjunction with medication behavioral health interventions. Life sciences organizations should collect evidence that demonstrates how medication and non-medication behavioral health interventions interact to impact efficacy of their products. Providers and patients also need that information to understand efficacy and trade-offs across the care continuum.

c) Develop partnerships with payers and providers to evaluate how data can be layered together to supplement existing data for innovative purposes. For example, integrating longitudinal remote patient monitoring and wearable data in behavioral health treatment can provide valuable insight into behavioral health status and risk factors preceding adverse episodes. Use this data to develop care goals and end points, establish clinical guidelines, and inform coverage of behavioral health interventions. Work together to understand what recovery looks like in practice to foster better coverage decisions.

What is the problem?

Life sciences organizations depend on behavioral health professionals to incorporate life sciences solutions into the treatment plans they recommend to patients and influence adherence and efficacy of treatment. To ensure solutions are accessible to consumers, life sciences organizations should invest in building a behavioral health workforce that is best positioned to identify the right solutions for behavioral health patients and is equipped to support the best intervention outcomes. By doing so, life sciences can directly impact variability in care delivery.

What can life sciences organizations do?

1. Strengthen diversity, equity, and inclusion efforts within your own organization first. Access begins at fair, representative, diverse, and decentralized clinical trials. But the teams designing those trials also need to represent a diverse population. We must reduce bias at the start to advance accessibility and equity across the patient care journey.

a) Staff at all levels, including business leaders, should represent the community across all domains of diversity.

b) HR leaders must redesign talent management processes by focusing on historically marginalized employees, including with salary adjustments, equitable mentorship, and advancement opportunities.

c) Executives should equip staff and leaders to embrace discomfort and hold each other accountable for creating a culture where all employees feel included, respected, and valued.

2. Invest in equipping behavioral health professionals with the knowledge, skill set, and real-world evidence to appropriately advocate for and prescribe treatments. Professionals who are appropriately skilled and empowered with the right clinical and social/behavioral knowledge can make the difference between patients accessing a treatment that is well-suited for their condition or harmful to them. This impacts challenges like underutilization of potentially impactful solutions and harmful overutilization of pharmaceuticals. Life sciences organizations should:

a) Offer training programs to providers to contextualize your solution, demonstrate appropriate use cases, and understand the patient population

b) Help customers identify eligible patients in their population, with a special focus on patients who may often be overlooked.

c) Support the creation of clinical guidelines and standards that incorporate real-world evidence.

3. Support building diverse pipelines for behavioral health professionals, including para-professionals and social workers. This may feel atypical to life sciences’ role in the behavioral health industry, but many life sciences organizations are already investing in workforce pipelines for physical health research. Having a diverse behavioral health workforce helps patients of similar backgrounds and identities develop trust in the health system and in treatments. A few ways to bolster the work:

a) Provide scholarships for behavioral health professionals in training who are from underrepresented backgrounds.

b) Partner with local educational institutions to offer/fund internship programs, apprenticeship, and mentorship opportunities for students from underrepresented backgrounds.

What is the problem?

Social determinants of health have wide-reaching and detrimental effects. In the behavioral health space, this is compounded by the fact that behavioral health issues and mental illness are more common in disadvantaged communities. SDOH impact not only the acuity of the behavioral health condition, but also a patient’s ability to successful engage and adhere to recommended treatment plants—including life sciences solutions. Addressing non-clinical needs and social determinants of health can amplify the impact of behavioral health interventions and improve outcomes. Life sciences organizations can make large-scale impact on the accessibility of SDOH data and partner to invest in internal programs and external organizations that address the social determinants of health.

What can life sciences organizations do?

1. Refine your existing evidence strategy to include SDOH and health equity data. Life sciences organizations should partner with an inclusive group of cross-industry stakeholders (including payers, providers, and digital health entities) to evaluate evidence gaps and design evidence-generation plans to fill them.

a) Incorporate SDOH data at the beginning of the product lifecycle, rather than as an afterthought. To do this, life sciences organizations should evaluate their analytic capability. Use our health equity measurement conversation guide with cross-functional teams at your organization to assess your current analytics infrastructure for collecting and integrating SDOH data. Starting with these conversations helps to avoid common pitfalls created when SDOH data is collected in an ad hoc and incomplete manner. SDOH data can have actionable utility in research and informing care delivery processes. It should be incorporated across the product lifecycle to identify barriers to participation in clinical trials, identify barriers to treatment, and inform real-world impacts on efficacy.

b) Expand evidence strategy to recognize and quantify the role SDOH plays in health outcomes. Structure data collection, from clinical trials to post-market studies, to collect data beyond REGAL (race, ethnicity, gender identity and sexuality, age, and language) data to consider additional intersections and factors. Data-sharing partnerships can supplement existing data to help close gaps and reach strategic goals.

2. Partner with and invest in community-based organizations (CBOs) to support the provision of services related to SDOH. To maximize the benefits and outcomes of life sciences products, support community-based organizations that provide services that reduce non-clinical barriers to behavioral health treatment via social services. Fortunately, there are numerous existing organizations providing such support to patients that life sciences organizations can partner with, leverage, support, and invest in.

a) Identify and connect with CBOs that already focus on and have relationships with priority populations. Start by defining your priority populations. Who are they? Where are they? What impedes market access in this population? Then, connect with community organizations that provide social care services to that group. With access to community support infrastructure, life sciences organizations can support their most vulnerable consumers and improve adherence and outcomes at scale.

b) Partner with CBOs to build the evidence base for SDOH-related interventions to improve population health and community impact. Life sciences organizations may be able to drive these initiatives alone or may use this as an opportunity to collaborate with other stakeholders in a shared community. Working with community organizations to provide robust, data-driven insights and measure the impact of specific social care interventions—like providing transportation or food—can enable community organizations to improve outcomes in cost-effective ways. Designing initiatives that allow life sciences organizations to garner data-driven SDOH insights to direct, track, and measure the impact of their interventions helps ensure dollars are spent effectively and efficiently.

What is the problem?

Societal discrimination against people with behavioral health conditions prevents many individuals from seeking care. And individuals from marginalized groups often face even greater stigma around accessing behavioral health care, exacerbating inequities in access and outcomes.

Because bias against people with behavioral health conditions is so ingrained in our society, it often influences interactions with patients. On a structural level, life sciences organizations may not be set up to effectively meet the needs of individuals with behavioral health conditions, as outcomes are often harder to track. On an individual level, some staff members may not have sufficient knowledge about behavioral health conditions or may hold unconscious bias against people with behavioral health conditions.

What can life sciences organizations do?

1. Regularly collect input from individuals with behavioral health conditions (including with internal employee resource groups) and apply that input to clinical trial design. Cultural stigma and internal bias can have detrimental effects on tools and products life sciences organizations design. An inclusive work environment leads to inclusive product design and thus better outcomes. To better incorporate individual experiences, life sciences organizations should establish a protocol for implementing feedback and closing the feedback loop. After getting feedback from individuals, make sure that it is recorded. Review feedback regularly and create time-bound plans to act on it. Follow up with individuals to let them know how their feedback is being used.

2. Provide behavioral health literacy training to all leaders and employees. Behavioral health literacy training, such as mental health first aid and the Just Five program, promotes a culture of acceptance around mental health conditions and substance use disorders. Training like this also improves leaders’ and employees’ understanding of care options. This culture of acceptance is foundational to creating a trauma-informed environment in the workplace. Training should promote an accurate understanding of behavioral health conditions, including their causes and prevalence. This information can normalize behavioral health needs and help people understand them as health conditions rather than personal weaknesses. Ensure that training helps leaders and managers learn to identify signs of emotional distress and connect team members with resources for support.