Workplace Wellbeing

How Data-Driven Provider Matching Helps Members Get Better Faster

AI is improving mental healthcare by matching people with the best provider for their unique needs, helping them get better faster.

Written by
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Emmanuel Matthews
Director of Product
Clinically reviewed by
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    The role of artificial intelligence in mental healthcare

    Rapid advances in artificial intelligence (AI) have been dominating news headlines, and you may be wondering how it’s being used to improve mental healthcare.

    At its broadest definition, AI combines computer science and large datasets to enable problem-solving, which has branched off into many applications across a variety of subfields. The goal is to build generalizable systems that can mimic human intelligence, which is known as artificial general intelligence (AGI).

    Machine learning is the core component of AI that's most often applied to mental healthcare. It involves designing a system and algorithms that can identify new patterns and insights from large data sets, that we could not see with traditional ways of looking at data. 

    In mental healthcare, machine learning can be used to improve treatment outcomes by using large amounts of data to help match people with the best provider for their unique needs. Let’s explore how this works.

    What is data-driven matching?

    To utilize data and machine learning for better mental health treatment outcomes, there needs to be an initial benchmark. Clinical scales are the benchmarking tools clinicians and researchers use to measure mental health symptoms.

    For example, an individual seeking help with a mental health condition fills out a set of standard clinical questions when entering care to provide information about their symptoms. That’s the first data source. 

    When matching a member with the ideal provider, it’s also important to take into account factors such as:

    1. Type of treatment the member is looking for
    2. Member demographics
    3. The provider’s cultural competency
    4. Social determinants of health
    5. Member preferences like: gender, similar lived experience, etc.

    All this data can be run through machine learning models, which are able to match the member with the best provider for their needs.

    Why is provider fit so important?

    A central component of patient-provider fit is therapeutic alliance. Research has demonstrated that therapeutic alliance is a more reliable predictor of outcomes than therapeutic approach, and drives 45-50% of therapeutic outcomes. 

    This implies that the world’s greatest clinical team can’t maximize their effectiveness if the patient doesn’t feel comfortable or confident that the provider can help them, or if they don’t agree with the recommended treatment plan. 

    So, how can we find the best fit if we know that most providers are able to do a good job with providing treatment? How do we drive useful engagement in care? How do we get people better, faster?

    Getting provider fit right, the first time

    Ranking providers on a scale of general effectiveness is too simplistic. It ignores the reality that each client has unique needs that have to be addressed, and not every provider is equipped to treat every client. 

    We have to input more detail into the algorithms we use to figure out how to match providers and members in a way that creates an optimal treatment pair. 

    Using machine learning to analyze all these pieces of data helps us create a system that assesses multiple inputs like demographics, social determinants of health, clinical data, and other items. Then we can build a system that identifies the ideal provider fit in a way that resonates with the person seeking care. 

    Data-driven matching also sets providers up for success. We know they want to help—that’s why they chose this profession. If we can identify a good client-provider match, then the provider is able to see a client they have a higher chance of helping. This improves overall outcomes and helps providers feel more successful in their work.

    Getting provider fit wrong can drive people away from care

    If a member is matched with a provider who isn’t a good fit, what happens? Well, that person might have a bad experience that makes them hesitant to seek care again. For someone experiencing mental health challenges, that’s a really negative and high risk outcome.

    There’s something synergistic about an ideal member-provider pairing, where the client-provider alliance builds fast, and allows the client to feel comfortable being open and vulnerable enough to talk about difficult parts of their life.

    The mental health journey can feel really lonely. To feel seen, heard, understood, and supported on that journey goes a long way. That’s why we have to get better at matching people seeking mental healthcare with the right provider.

    Data-driven matching improves ROI

    Giving members access to a mental healthcare solution that optimizes clinical improvement drives down costs on total health spend, while also providing care that actually works. This delivers financial ROI and improves mental health for your members. 

    The goal for mental healthcare solutions is to help members feel better, faster. Until now, we simply haven’t had the cohesive healthcare data platforms or the tools to generate the high-quality data required to get matching right. 

    This has made it more likely that a member bounces back and forth between multiple providers, trying to find something that works, or worse—giving up and not receiving the needed care. These drop-outs from care are one of the largest barriers to achieving consistent ROI for a mental health program. 

    Better outcomes for underrepresented groups

    Health equity is another important component of using data-driven matching for mental healthcare solutions. When looking at the needs of your members, underrepresented groups are often overlooked.

    If a marginalized group only represents 10% of the population, then a generic provider network may not be suitable to meet all their needs. 

    With the precision of data-driven matching, and by composing a provider network that suits member’s needs, you can ensure there aren’t gaps for underrepresented populations that may have unique challenges or needs.

    Spring Health’s approach to data-driven matching

    Each new member has the option to discuss their mental health journey with a Care Navigator, who is a master’s level, licensed clinician. Or they can begin by completing our clinically validated assessment. This takes 3-5 minutes and gives us that first set of data points so we can match the member with an ideal provider.

    The member’s preferences for a provider can also be taken into account before they schedule an appointment. For example, if a member would like a Black female provider who works with parents, they can apply those filters within our platform.

    Using data to help solve a very human problem

    The ideal mental health solution for an organization or health plan uses data and new machine learning techniques to help solve the complex problem of treating mental health issues—while at the same time, recognizing the inherent human complexity of life.

    We’re not just throwing data at a problem because we have these powerful technologies. We center our outlook on the deeply human experiences of struggling with mental health, something that is often complex, stigmatized, lonely, and difficult. 

    There are already so many hurdles to jump for people who are trying to get help. Because of that, we have to try and get provider fit right the first time, using the most powerful, data-driven matching tools possible.

    Learn more about how a data-driven approach optimizes engagement, drives clinical outcomes, and helps members improve their mental health twice as fast.

    About the Author
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    Emmanuel Matthews
    Director of Product

    Emmanuel leads Spring Health's Precision Mental Health team, building data and machine learning products that help deliver better and faster care. He has spent the last decade developing and scaling nascent technologies and emerging products, ranging from wearables to AI in the healthcare, EdTech, and consumer hardware industries. Emmanuel has also served as a delegate of the United States Technical Advisory Group for Artificial Intelligence Standards and as an innovation and startup advisor. He is a fervent champion of equity and inclusion, and committed to helping improve access and outcomes for underserved communities.

    About the clinical reviewer
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