This is Part 5 of our blog series on Improving Access with a Leading Provider Network.
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 and 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 that 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:
Type of treatment the member is looking for
The provider’s cultural competency
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 provider fit is therapeutic alliance. Research has demonstrated that therapeutic alliance—patient-provider fit—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 won’t maximize their effectiveness if the patient doesn’t feel comfortable or confident that the provider can help them or if they do not agree with the process that will be used to treat them.
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, and ignores the reality that effectiveness is also deeply related to fit. 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.
To get that level of granularity, we look at additional provider characteristics, aside from whether they’re highly rated or not. For example, given the composition (things like demographics and social determinants of health) of members and what mental health conditions they have, we should also take into account:
Does a provider keep similar clients engaged in care long enough to have a chance to get better?
How well and how quickly has a provider helped similar clients recover?
Does the provider have the right skill sets to address a client’s specific needs?
Using machine learning to analyze all these pieces of data helps us create a system that can look at multiple inputs like demographics, social determinants of health, clinical data, and number of appointments. 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. It’s possible to maximize their impact, by identifying what group of patients the provider is best suited to treat, using that more granular data.
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 therapeutic care again. For someone experiencing mental health challenges, that’s a really negative outcome. The risk is high.
There’s something synergistic about an ideal member-provider pairing, where the provider is able to articulate a treatment plan that allows their client to feel comfortable being vulnerable and peeling back the layers of their mind, emotions, and experiences.
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 maximizes therapeutic outcomes
To find a provider network that fits your members’ needs, and is effective in treating mental health challenges, what should you look for?
You could simply look for a large network with high quality providers and hope for the best results. But as we discussed earlier, even if providers are good at what they do, that doesn’t necessarily mean they’ll be able to most effectively meet the needs of every population.
To get the most effective care, you have to be able to find the right composition of providers. For a mental health solution to actually work, it needs a built-in understanding of which characteristics are important to maximize therapeutic outcomes for a given population.
This approach removes the need to use guesswork or word of mouth to figure out which provider can most effectively help each member.
It’s really difficult to find consistent rating systems for healthcare providers amid the variety of available websites with inconsistent ranking and review approaches. Too many people enter care with no idea if it’s going to work for their specific needs.
Data-driven matching improves ROI
Giving members access to a mental healthcare solution that optimizes clinical improvement drives down costs on health spend while also providing care that actually works. This delivers financial ROI while also improving 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 quantity of 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.
They may even get frustrated and decide that mental healthcare just isn’t right for them.
Better outcomes for underrepresented groups
Health equity is another important component of using data-driven matching for mental healthcare solutions. While looking at the needs of your members, underrepresented groups are often overlooked.
If a marginalized group only represents 10% of the population, then most likely, a generic provider network isn’t going to be suitable to meet their needs.
With the precision of data-driven matching and composing a provider network that suits member’s needs, you can ensure there aren’t gaps for underrepresented populations who may have unique 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 and utilizes cognitive behavioral therapy, they are able to 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.
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 we have.
Learn more about how a data-driven approach optimizes engagement, drives clinical outcomes, and helps members improve their mental health twice as fast.
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